#关闭警告信息
import warnings
warnings.filterwarnings("ignore")
#测试GPU可用性
import tensorflow as tf
print('GPU可用性:',tf.test.is_gpu_available())
WARNING:tensorflow:From /tmp/ipykernel_822/2381302516.py:3: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
GPU可用性: True
2025-05-31 18:12:45.081025: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 AVX512F FMA To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2025-05-31 18:12:46.052509: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1532] Created device /device:GPU:0 with 30987 MB memory: -> device: 0, name: Tesla V100-PCIE-32GB, pci bus id: 0000:67:02.0, compute capability: 7.0
读取数据
#加载预处理后的数据
import pandas as pd
trainmap = pd.read_csv('/mnt/workspace/DNA_methylation_Data/trainmap.csv')
traindata = pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_Data.pkl')
dna_methylation_cg=pd.read_excel('/mnt/workspace/DNA_methylation_Data/Analysis_Data_pkl/DNA_Methylation_CG.xlsx')
#拼接CG编号
CG_ID_str=['sample_id']+list(dna_methylation_cg.loc[:,'cpgsite'])
traindata.columns=CG_ID_str
traindata.head(10)
| sample_id | cg00050873 | cg00212031 | cg00213748 | cg00214611 | cg00455876 | cg01707559 | cg02004872 | cg02011394 | cg02050847 | ... | cg12735498 | cg12737588 | cg12738347 | cg12758090 | cg12766407 | cg12789522 | cg12793879 | cg12794168 | cg12799119 | cg12848808 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | train10001 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.096055 | 4.051632 | -0.355631 | 1.157883 | -9.210440 | -2.921730 | -3.472874 | 1.202370 | -5.773449 | -0.164336 |
| 1 | train10002 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.451424 | 4.178048 | -0.144221 | 0.944243 | -3.938986 | -2.732410 | -3.406479 | 0.866223 | -4.489850 | -0.285874 |
| 2 | train10003 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.241113 | 4.402578 | -0.562248 | 0.924494 | -5.773449 | -2.767818 | -3.619579 | 1.098346 | -4.585271 | -0.326813 |
| 3 | train10004 | NaN | NaN | NaN | NaN | NaN | -1.398461 | NaN | NaN | NaN | ... | -0.184485 | 3.744756 | -1.087711 | 1.515914 | -5.273603 | -3.580953 | -3.284902 | 1.179996 | -4.489850 | -0.570906 |
| 4 | train10005 | 1.136022 | -4.489850 | NaN | -3.993781 | 1.312567 | -2.901295 | -4.585271 | 2.804368 | 2.985388 | ... | -0.160310 | 3.744756 | -0.866223 | 1.529517 | -5.273603 | -2.823096 | -3.406479 | 1.294708 | -4.690541 | -0.132166 |
| 5 | train10006 | 1.423834 | -9.210440 | 1.035353 | -3.837361 | 1.288795 | -3.100385 | -5.093549 | 2.767818 | 1.848299 | ... | 0.003999 | 4.178048 | -1.536363 | 1.130596 | -9.210440 | -2.804368 | -3.619579 | 1.157883 | -4.807960 | -0.885489 |
| 6 | train10007 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.092047 | 4.247583 | -0.200630 | 1.613763 | -5.093549 | -2.861514 | -3.543689 | 1.109037 | -5.773449 | -0.164336 |
| 7 | train10008 | 1.475799 | -3.993781 | NaN | -4.051632 | 1.475799 | -3.314031 | -4.807960 | 2.785946 | 2.196336 | ... | -0.241113 | 4.402578 | -0.532105 | 1.550147 | -5.093549 | -3.100385 | -3.374769 | 1.125184 | -4.489850 | 0.220849 |
| 8 | train10009 | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | NaN | ... | -0.212757 | 3.938986 | -1.342748 | 1.423834 | -4.585271 | -3.007447 | -3.472874 | 1.208006 | -4.247583 | -0.502197 |
| 9 | train10010 | 1.515914 | -4.051632 | NaN | -4.585271 | 1.174443 | -2.681278 | -4.112908 | 3.406479 | 2.069693 | ... | -0.372163 | 4.807960 | -0.570906 | 1.423834 | -6.811545 | -2.804368 | -3.472874 | 1.136022 | -4.247583 | -0.703513 |
10 rows × 100001 columns
数据预处理:处理空缺数据、数据拼接、转化数据类型
#统计数据空缺值
traindata_null=traindata.isnull().sum().sum() #统计前10万个特征缺失值
data_sum=traindata.shape[0]*traindata.shape[1]
traindata_null_rate=traindata_null/data_sum #计算特征缺失率
print('训练数据缺失数据量为:{0},数据总量为:{1}'.format(traindata_null,data_sum))
print('训练数据缺失率为:{0}%'.format(round(traindata_null_rate*100,4)))
训练数据缺失数据量为:46058632,数据总量为:823308233 训练数据缺失率为:5.5943%
#缺失数据处理
traindata.fillna(0,inplace=True) #训练集预处理
# 拼接数据集
traindata=traindata.merge(trainmap[['sample_id', 'age', 'gender', 'sample_type', 'disease']],on='sample_id',how='left')
pd.isnull(traindata).sum()
sample_id 0
cg00050873 0
cg00212031 0
cg00213748 0
cg00214611 0
..
cg12848808 0
age 0
gender 69
sample_type 0
disease 0
Length: 100005, dtype: int64
traindata['gender'].value_counts()
gender F 4409 M 3755 Name: count, dtype: int64
traindata.shape
(8233, 100005)
#统计数据中患病数据和患病数据汇总
sample_type_sum=traindata['sample_type'].value_counts() #统计患病数据总量
print('-----------------患病数据总量----------------')
print(sample_type_sum)
disease_sum=traindata['disease'].value_counts() #汇总患病数据
print('-----------------患病数据汇总----------------')
print(disease_sum)
-----------------患病数据总量---------------- sample_type control 6266 disease tissue 1967 Name: count, dtype: int64 -----------------患病数据汇总---------------- disease control 6266 Alzheimer's disease 737 schizophrenia 381 Parkinson's disease 266 rheumatoid arthritis 159 stroke 147 Huntington's disease 135 Graves' disease 58 type 2 diabetes 46 Sjogren's syndrome 38 Name: count, dtype: int64
#读取卒中DNA甲基化数据
Stroke_Data=traindata.loc[traindata.loc[:,'disease']=='stroke',:]
Stroke_Data.shape
(147, 100005)
#读取卒中DNA甲基化数据
Normal_Data=traindata.loc[traindata.loc[:,'disease']=='control',:]
Normal_Data.shape
(6266, 100005)
#整合数据
Analysis_Data=pd.concat([Stroke_Data,Normal_Data],axis=0)
Analysis_Data.shape
(6413, 100005)
#数据类型转化
disease_mapping = {
'control': 0,
'stroke': 1,
} #构建数据对应关系
sample_type_mapping = {'control': 0, 'disease tissue': 1}
gender_mapping = {'F': 0, 'M': 1}
#训练集转化
Analysis_Data['disease_encode']=Analysis_Data['disease'].map(disease_mapping)
Analysis_Data['sample_type_encode']=Analysis_Data['sample_type'].map(sample_type_mapping)
Analysis_Data['gender_encode']=Analysis_Data['gender'].map(gender_mapping)
#输出仅包含卒中和正常组的数据
Analysis_Data.to_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Analysis_Data.to_csv('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.csv')
读取数据
import pandas as pd
Data=pd.read_pickle('/mnt/workspace/DNA_methylation_Data/Stroke_Data/Stroke_Data.pkl')
Data.head(10)
| sample_id | cg00050873 | cg00212031 | cg00213748 | cg00214611 | cg00455876 | cg01707559 | cg02004872 | cg02011394 | cg02050847 | ... | cg12794168 | cg12799119 | cg12848808 | age | gender | sample_type | disease | disease_encode | sample_type_encode | gender_encode | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 8040 | train18041 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | ... | 0.804620 | -3.406479 | -1.991711 | 78.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 8041 | train18042 | 2.049755 | -3.472874 | 0.000000 | -3.837361 | 0.974195 | -2.375206 | -3.659672 | 3.790034 | 4.690541 | ... | 0.871025 | -3.993781 | -1.642924 | 52.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8042 | train18043 | 2.196336 | -2.985388 | 0.000000 | -2.963760 | 0.984269 | -2.324893 | -3.790034 | 3.790034 | 4.247583 | ... | 0.994389 | -4.051632 | -2.059685 | 92.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8043 | train18044 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -1.242189 | 0.000000 | 0.000000 | 0.000000 | ... | 1.392179 | -3.886935 | -2.648415 | 87.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 8044 | train18045 | 2.632328 | -3.284902 | 2.120936 | -3.938986 | 1.628272 | -2.616463 | -3.886935 | 2.942546 | 4.247583 | ... | 1.621000 | -3.701353 | -2.681278 | 87.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8045 | train18046 | 2.264792 | -2.942546 | 0.000000 | -3.052934 | 0.979226 | -1.972920 | -3.659672 | 3.543689 | 4.489850 | ... | 1.191150 | -3.619579 | -1.991711 | 41.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8046 | train18047 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -1.098346 | 0.000000 | 0.000000 | 0.000000 | ... | 1.436670 | -3.744756 | -2.120936 | 78.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 8047 | train18048 | 1.963624 | -4.178048 | 0.000000 | -4.178048 | 0.124134 | -2.324893 | -5.492861 | 3.076403 | 4.940737 | ... | 1.635580 | -3.837361 | -1.954393 | 80.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8048 | train18049 | 1.874054 | 0.000000 | 0.000000 | 0.000000 | -0.298130 | -1.798190 | -4.112908 | 3.993781 | 4.585271 | ... | 1.703036 | -3.406479 | -2.131436 | 69.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8049 | train18050 | 1.462650 | -3.256540 | 0.000000 | -4.051632 | 0.000000 | -2.163468 | -5.273603 | 2.921730 | 4.402578 | ... | 1.489058 | -2.785946 | -1.963624 | 75.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
10 rows × 100008 columns
数据预处理和数据类型转化已经完成,随后进行特征选择。首先使用PCA算法计算保留95%和99%信息的维度,并将数据降维至3维可视化数据分布。对甲基化位点采用方差过滤,高相关过滤和lasso回归.
#采用PCA算法计算保留95%和99%信息的weidu
Methylation=Data.iloc[:,1:-7]
Methylation.shape
(6413, 100000)
#执行PCA算法-计算保留99%信息时的维度
from sklearn.decomposition import PCA
Methylaion_PCA=PCA(n_components=0.99)
PCA_Methylation_99=Methylaion_PCA.fit_transform(Methylation) #数据降维
PCA_Methylation_99.shape
#计算可解释方差比
import numpy as np
import matplotlib.pyplot as plt
Explain_variance=np.cumsum(Methylaion_PCA.explained_variance_ratio_)
#绘图
Explain_variance_plt=plt.figure(dpi=300)
Epl_variance=Explain_variance_plt.add_subplot(111)
Epl_variance.set_title('Explainable variance ratio curve')
Epl_variance.grid(color='black',linestyle='-.',alpha=0.2)
Epl_variance.plot(np.arange(1,len(Explain_variance)+1,1,dtype=int),Explain_variance,color='orange',linestyle='-')
#添加95%可解释方差曲线
Epl_variance.scatter(x=2500,y=0.95,color='green',marker='o')
Epl_variance.axhline(y=0.95, color='green', linestyle='-.')
Epl_variance.axvline(x=2500, color='green', linestyle='-.', label='Retain 95 per cent of explainable variance')
#添加99%可解释方差曲线
Epl_variance.scatter(3973,0.99,color='red',marker='o')
Epl_variance.axhline(y=0.99, color='red', linestyle='-.')
Epl_variance.axvline(x=3973, color='red', linestyle='-.', label='Retain 99 per cent of explainable variance')
Epl_variance.set_xlabel('n_components')
Epl_variance.set_ylabel('explained_variance_ratio')
plt.legend()
plt.show()
#使用PCA将数据降维至3维
from sklearn.decomposition import PCA
from pandas import DataFrame
PCADisplay3D=PCA(n_components=3)
Methylation3D_PCA=PCADisplay3D.fit_transform(Methylation) #PCA3D降维
Methylation3D_PCA=DataFrame(Methylation)
Methylation3D_PCA['Disease_Encoder']=Data.loc[:,'disease_encode']
#绘制3D散点图
import matplotlib.pyplot as plt
from mpl_toolkits.mplot3d import Axes3D
NormalData=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==0,:]
Stroke_Data=Methylation3D_PCA.loc[Methylation3D_PCA.loc[:,'Disease_Encoder']==1,:]
OA_X,OA_Y,OA_Z=NormalData.iloc[:,0],NormalData.iloc[:,1],NormalData.iloc[:,2]
CON_X,CON_Y,CON_Z=Stroke_Data.iloc[:,0],Stroke_Data.iloc[:,1],Stroke_Data.iloc[:,2]
# 创建3D图形对象
fig=plt.figure(dpi=300)
ax=fig.add_subplot(111, projection='3d')
ax.scatter(CON_X,CON_Y,CON_Z,color='green',label='Stroke')
ax.scatter(OA_X,OA_Y,OA_Z,color='orange',label='Normal')
# 设置标签
ax.set_xlabel('Faeture 0')
ax.set_ylabel('Feature 1')
ax.set_zlabel('Feature 2')
plt.legend()
# 绘制投影到XY平面
plt.figure()
plt.title('XY plane projection')
plt.scatter(OA_X,OA_Y,color='orange',label='Normal')
plt.scatter(CON_X,CON_Y,color='green',label='Stroke')
plt.xlabel('Feature 0')
plt.ylabel('Feature 1')
plt.legend()
# 绘制投影到XZ平面
plt.figure()
plt.title('XZ plane projection')
plt.scatter(OA_X,OA_Z,color='orange',label='Normal')
plt.scatter(CON_X,CON_Z,color='green',label='Stroke')
plt.xlabel('Feature 0')
plt.ylabel('Feature 2')
plt.legend()
# 绘制投影到YZ平面
plt.figure()
plt.title('YZ plane projection')
plt.scatter(OA_Y,OA_Z,color='orange',label='Normal')
plt.scatter(CON_Y,CON_Z,color='green',label='Stroke')
plt.xlabel('Feature 1')
plt.ylabel('Feature 2')
plt.legend()
plt.show()
#对数据采取低方差过滤算法
from pandas import DataFrame
from sklearn.feature_selection import VarianceThreshold
Variance_Selector=VarianceThreshold(threshold=0.5)
VarSele_Data=Variance_Selector.fit_transform(Methylation) #执行低方差过滤
VarSele_Data=DataFrame(VarSele_Data)
VarSele_Data.columns=Variance_Selector.get_feature_names_out()
VarSele_Data.head(10)
| cg00050873 | cg00212031 | cg00214611 | cg01707559 | cg02004872 | cg02011394 | cg02050847 | cg02233190 | cg02494853 | cg02839557 | ... | cg12700439 | cg12711755 | cg12712409 | cg12719753 | cg12725420 | cg12727528 | cg12732791 | cg12737588 | cg12766407 | cg12848808 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -6.163916 | 0.000000 | ... | 2.715111 | 2.921730 | 1.417454 | 2.804368 | -4.112908 | -5.773449 | 1.152395 | 5.773449 | -5.492861 | -1.991711 |
| 1 | 2.049755 | -3.472874 | -3.837361 | -2.375206 | -3.659672 | 3.790034 | 4.690541 | -3.701353 | -3.406479 | -3.886935 | ... | 2.375206 | 3.619579 | 2.241551 | 2.600812 | -2.349789 | -4.051632 | 0.909791 | 4.489850 | -4.690541 | -1.642924 |
| 2 | 2.196336 | -2.985388 | -2.963760 | -2.324893 | -3.790034 | 3.790034 | 4.247583 | -3.701353 | -3.790034 | -2.901295 | ... | 2.985388 | 2.324893 | 2.049755 | 2.100192 | -1.848299 | -4.178048 | 1.045719 | 4.690541 | -5.273603 | -2.059685 |
| 3 | 0.000000 | 0.000000 | 0.000000 | -1.242189 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -4.322159 | 0.000000 | ... | 2.570129 | 2.388119 | 2.010774 | 2.300497 | -2.264792 | -3.993781 | 1.423834 | 4.402578 | -5.093549 | -2.648415 |
| 4 | 2.632328 | -3.284902 | -3.938986 | -2.616463 | -3.886935 | 2.942546 | 4.247583 | -4.585271 | -3.837361 | -2.525567 | ... | 2.388119 | 3.659672 | 1.695385 | 2.585369 | -2.059685 | -4.322159 | 1.848299 | 5.492861 | -4.585271 | -2.681278 |
| 5 | 2.264792 | -2.942546 | -3.052934 | -1.972920 | -3.659672 | 3.543689 | 4.489850 | -2.540233 | -2.823096 | -2.985388 | ... | 3.029956 | 3.507691 | 2.349789 | 2.230092 | -2.069693 | -4.690541 | 1.152395 | 4.807960 | -5.773449 | -1.991711 |
| 6 | 0.000000 | 0.000000 | 0.000000 | -1.098346 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -4.178048 | 0.000000 | ... | 2.732410 | 3.284902 | 1.814692 | 2.264792 | -2.230092 | -4.402578 | 1.823018 | 4.690541 | -5.273603 | -2.120936 |
| 7 | 1.963624 | -4.178048 | -4.178048 | -2.324893 | -5.492861 | 3.076403 | 4.940737 | -2.401172 | -3.619579 | -3.149987 | ... | 2.555085 | 3.619579 | 1.790011 | 2.555085 | -1.848299 | -4.402578 | 1.300641 | 5.273603 | -5.093549 | -1.954393 |
| 8 | 1.874054 | 0.000000 | 0.000000 | -1.798190 | -4.112908 | 3.993781 | 4.585271 | -3.659672 | -3.314031 | -3.149987 | ... | 2.767818 | 3.472874 | 1.522701 | 2.570129 | -1.781880 | -4.489850 | 0.949208 | 4.940737 | -5.273603 | -2.131436 |
| 9 | 1.462650 | -3.256540 | -4.051632 | -2.163468 | -5.273603 | 2.921730 | 4.402578 | -2.511081 | -2.921730 | -2.942546 | ... | 2.767818 | 2.555085 | 1.945225 | 3.149987 | -1.404768 | -6.163916 | 0.000000 | 4.489850 | -5.773449 | -1.963624 |
10 rows × 63575 columns
#构建特征方差数据表
VarianseData=DataFrame()
VarianseData['MetaBolite']=Variance_Selector.feature_names_in_
VarianseData['Variances']=Variance_Selector.variances_
VarianseData.shape
(100000, 2)
#输出甲基化位点方差值
VarianseData.to_excel('/mnt/workspace/Run_Result/Vraince_result.xlsx')
#执行基于F分布的方差分析进行高相关过滤
from sklearn.feature_selection import SelectKBest, f_classif
Selectk=SelectKBest(score_func=f_classif,k=3973)
FselectorData=Selectk.fit_transform(VarSele_Data,Data.loc[:,'disease_encode'])
FselectorData=pd.DataFrame(FselectorData)
feature_nameindex=Selectk.get_support(indices=True) #获取特征索引
feature_names=VarSele_Data.columns #获取特征名
Kfeature_names=[feature_names[i] for i in feature_nameindex]
FselectorData.columns=Kfeature_names
FselectorData.head(10)
| cg01404988 | cg02393514 | cg03315431 | cg04065558 | cg04294190 | cg04490290 | cg04630982 | cg04667267 | cg04931939 | cg05032098 | ... | cg11183227 | cg11353598 | cg11540553 | cg12031275 | cg12160578 | cg12309653 | cg12368188 | cg12379383 | cg12414181 | cg12627870 | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -1.536363 | -1.557086 | 1.146922 | -4.807960 | 1.695385 | -1.141464 | -0.132166 | -0.934347 | -2.785946 | -0.540693 | ... | 0.739857 | -1.179996 | 4.112908 | -0.566575 | -2.142023 | -4.112908 | -3.938986 | -2.388119 | -2.664729 | -3.100385 |
| 1 | -2.496772 | -2.241551 | 2.152700 | -4.489850 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -1.790011 | ... | 1.354970 | -2.218738 | 3.701353 | -1.522701 | -1.954393 | -3.076403 | -3.886935 | -1.749816 | -2.362431 | -2.749976 |
| 2 | -2.482634 | -1.628272 | 2.288480 | -3.993781 | 0.204671 | -2.468664 | -1.680203 | -2.152700 | -2.901295 | -2.001208 | ... | 1.536363 | -2.163468 | 3.790034 | 0.188519 | -2.120936 | -2.901295 | -3.374769 | -1.710727 | -3.701353 | -2.616463 |
| 3 | -3.149987 | -1.954393 | 1.114405 | -4.112908 | 2.089945 | -0.188519 | -0.949208 | -1.174443 | -3.256540 | -0.851873 | ... | 2.100192 | -2.454856 | 3.406479 | -1.430239 | -2.881228 | -3.543689 | -3.886935 | -2.362431 | -3.938986 | -2.454856 |
| 4 | -2.921730 | -2.511081 | 2.525567 | -3.993781 | 1.592260 | -2.337277 | -1.856830 | -1.672671 | -3.228904 | -2.767818 | ... | 1.404768 | -2.010774 | 3.886935 | 0.823416 | -2.253116 | -3.007447 | -3.439160 | -1.814692 | -3.343970 | -2.414368 |
| 5 | -2.600812 | -1.650305 | 2.616463 | -4.051632 | 1.765757 | -3.201956 | -1.657722 | -1.650305 | -2.823096 | -1.991711 | ... | 2.441207 | -1.219330 | 4.489850 | -0.216802 | -2.648415 | -2.732410 | -3.314031 | -1.564056 | -1.991711 | -2.732410 |
| 6 | -2.861514 | -0.823416 | 0.989323 | -3.507691 | 2.030118 | -0.347383 | -0.100063 | 0.472506 | -2.881228 | -1.185564 | ... | 1.798190 | -2.349789 | 3.837361 | 3.580953 | -2.842141 | -4.112908 | -3.837361 | -2.496772 | -2.312634 | -2.648415 |
| 7 | -3.052934 | -2.454856 | 2.230092 | -3.439160 | -1.294708 | 0.000000 | -2.100192 | 0.000000 | -1.578093 | -2.362431 | ... | 1.430239 | -2.585369 | 3.374769 | 4.178048 | -1.814692 | -2.414368 | -3.886935 | -2.767818 | -2.312634 | -2.482634 |
| 8 | -2.496772 | -2.427712 | 2.276579 | -3.175661 | -0.964166 | 0.000000 | -1.695385 | 0.000000 | -2.375206 | -2.648415 | ... | 1.449607 | -2.375206 | 3.659672 | -1.806416 | -2.079779 | -2.414368 | -3.580953 | -2.441207 | -2.842141 | -2.715111 |
| 9 | -2.525567 | -2.600812 | 2.616463 | -3.284902 | -1.271175 | 0.000000 | 0.000000 | 0.000000 | 0.000000 | -1.918093 | ... | 1.152395 | -1.635580 | 3.507691 | 0.000000 | -1.606561 | -2.100192 | -3.029956 | -2.100192 | -2.069693 | -2.616463 |
10 rows × 3973 columns
#获取相关信息
KBestF_info=DataFrame()
KBestF_info['Feature']=Kfeature_names #载入特征
KBestF_info['scores']=Selectk.scores_[:3973]
KBestF_info['P value']=Selectk.pvalues_[:3973]
KBestF_info.shape
(3973, 3)
#输出相关计算结果
KBestF_info.to_excel('/mnt/workspace/Run_Result/FCorr_With_Methylation.xlsx')
#采用Lasso算法进行特征选择
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score
Target=Data.loc[:,'disease_encode']
# 数据标准化
scaler=StandardScaler()
X_scaled=scaler.fit_transform(FselectorData)
# 拆分数据集
X_train,X_test,y_train,y_test=train_test_split(X_scaled,Target, test_size=0.3, random_state=2024)
lasso_model = LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000) # 设置Lasso模型
lasso_model.fit(X_train,y_train) # 训练模型
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')#评估Lasso模型性能
print('Lasso模型训练集Accuracy为:',accuracy_score(y_train,lasso_model.predict(X_train)))
print('Lasso模型测试集Accuracy为:',accuracy_score(y_test,lasso_model.predict(X_test)))
Lasso模型训练集Accuracy为: 1.0 Lasso模型测试集Accuracy为: 0.9994802494802495
#查看模型混淆矩阵
import matplotlib.pyplot as plt
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
#查看训练集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_train,target=y_train)
#查看测试集混淆举证
Display_ConfusionMatrix(model=lasso_model,data=X_test,target=y_test)
import joblib
joblib.dump(lasso_model,'/mnt/workspace/Models/Lasso.pkl')
['/mnt/workspace/Models/Lasso.pkl']
#提取特征并进行特征选择
import numpy as np
LassoFeature_Index=np.where(lasso_model.coef_!=0)[1]
LassoFeature=FselectorData.iloc[:,LassoFeature_Index]
LassoFeature.shape
(6413, 70)
import matplotlib.pyplot as plt
Coef=plt.figure(dpi=300)
Coef_ax=Coef.add_subplot(111)
Coef_ax.grid(color='black',linestyle='-.',alpha=0.2)
Coef_ax.plot(LassoFeature_Index,pd.DataFrame(lasso_model.coef_[0][LassoFeature_Index]))
Coef_ax.set_xlabel('CG_index')
Coef_ax.set_ylabel('Coef')
Coef_ax.set_xticklabels(LassoFeature_Index)
plt.show()
/tmp/ipykernel_10044/197567409.py:8: UserWarning: set_ticklabels() should only be used with a fixed number of ticks, i.e. after set_ticks() or using a FixedLocator. Coef_ax.set_xticklabels(LassoFeature_Index)
使用Lasso验证DNA甲基化位点特征选择后的结果
#使用特征选择后的模型验证模型精度-输入[batchs,70],输出[batch,1]
X_TrainEv,X_testEv,y_trainEv,y_testEv=train_test_split(LassoFeature,Target, test_size=0.3, random_state=2024)
lasso_modelEvaluateD=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000)
lasso_modelEvaluateD.fit(X_TrainEv,y_trainEv)
LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. LogisticRegression(C=0.1, max_iter=10000, penalty='l1', random_state=2024,
solver='liblinear')#训练集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_TrainEv,y_trainEv)
#测试集混淆举证
Display_ConfusionMatrix(lasso_modelEvaluateD,X_testEv,y_testEv)
#使用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
from sklearn.metrics import accuracy_score
from sklearn.model_selection import KFold,cross_val_score
from sklearn.model_selection import cross_validate
def Model_Evelate_CV(Model,Data,Target):
cv=KFold(n_splits=10, shuffle=True, random_state=2024)
kv_scores =cross_validate(Model,Data,Target,cv=cv,scoring='accuracy',
return_train_score=True)
print('Fit_time:',kv_scores['fit_time'])
print('Mean Fit_time:',kv_scores['fit_time'].mean())
print('score_time:',kv_scores['score_time'])
print('Mean score_time:',kv_scores['score_time'].mean())
print('train_score:',kv_scores['train_score'])
print('Mean train_score:',kv_scores['train_score'].mean())
print('test_score:',kv_scores['test_score'])
print('Mean test_score:',kv_scores['test_score'].mean())
#用特征选择后的数据集再次训练模型评估特征选择效果-10折交叉验证
lasso_modelEvaluate=LogisticRegression(penalty='l1',solver='liblinear',C=0.1,random_state=2024,max_iter=10000) # 设置Lasso模型
Model_Evelate_CV(Model=lasso_modelEvaluate,Data=LassoFeature,Target=Target)
Fit_time: [0.15090775 0.20123768 0.1979394 0.19683361 0.19801974 0.19604516 0.19239807 0.17037344 0.16579437 0.18889141] Mean Fit_time: 0.1858440637588501 score_time: [0.00298166 0.01086545 0.00288725 0.00284386 0.00290656 0.00284505 0.00278687 0.00281239 0.00273204 0.00283217] Mean score_time: 0.003649330139160156 train_score: [0.99930688 0.9991336 0.99930688 0.9989605 0.99948025 0.999307 0.99948025 0.99913375 0.99948025 0.99913375] Mean train_score: 0.9992723102452785 test_score: [0.99844237 0.99844237 1. 0.99843994 0.99687988 0.99843994 1. 0.99687988 0.99843994 0.99687988] Mean test_score: 0.9982844173580029
#整合并输出特征选择结果
CG_Feature=Data.iloc[:,LassoFeature_Index]
DNA_Methylstion_Feature=pd.concat([CG_Feature,Data.iloc[:,-7:]],axis=1)
DNA_Methylstion_Feature.shape
(6413, 77)
DNA_Methylstion_Feature.to_excel('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.xlsx','UTF-8')
DNA_Methylstion_Feature.to_csv('/mnt/workspace/Run_Result/DNA_Methylstion_Feature.csv')
joblib.dump(lasso_modelEvaluateD,'/mnt/workspace/Models/lasso_modelEvaluate.pkl')
['/mnt/workspace/Models/lasso_modelEvaluate.pkl']
经过低方差过滤,基于F分布的相关分析和Lasso回归,获得了70个甲基化位点作为特征子集。
#读取DNA甲基化位点数据
import pandas as pd
MethylationData=pd.read_excel('Data/DNA_Methylstion_Feature.xlsx')
MethylationData.head(10)
| Unnamed: 0 | cg03706273 | cg00412368 | cg01370179 | cg01522249 | cg01999212 | cg03718079 | cg03977822 | cg05100261 | cg05876899 | ... | cg01446576 | cg01550055 | cg01717973 | age | gender | sample_type | disease | disease_encode | sample_type_encode | gender_encode | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 8040 | -4.112908 | -0.015997 | -1.557086 | 0.372163 | -2.525567 | -0.851873 | -0.124134 | -0.832866 | 2.921730 | ... | -2.570129 | -1.585160 | -3.052934 | 78.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 1 | 8041 | -2.767818 | -1.417454 | -1.417454 | 0.000000 | -4.585271 | -2.648415 | -0.489446 | -3.938986 | 3.228904 | ... | -3.744756 | -1.247949 | -3.886935 | 52.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 2 | 8042 | -3.374769 | -2.020410 | -1.529517 | 1.650305 | -4.112908 | -2.362431 | -0.430440 | -4.940737 | 3.076403 | ... | -4.051632 | -1.536363 | -5.273603 | 92.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 3 | 8043 | -4.807960 | -0.339147 | -1.814692 | -0.744433 | -2.985388 | -1.798190 | 0.298130 | -0.372163 | 3.374769 | ... | -4.585271 | -1.757764 | -4.178048 | 87.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 4 | 8044 | -4.402578 | -2.555085 | -1.665178 | 1.734052 | -3.507691 | -3.149987 | -0.128149 | -4.247583 | 3.228904 | ... | -4.247583 | -1.726235 | -3.993781 | 87.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 5 | 8045 | -3.149987 | -1.831393 | -1.599394 | 1.443126 | -2.732410 | -3.175661 | -0.076021 | -4.112908 | 3.314031 | ... | -4.112908 | -1.253727 | -4.112908 | 41.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 6 | 8046 | -3.619579 | -0.112095 | -1.119787 | -0.076021 | -2.823096 | -1.982282 | 0.164336 | -0.019997 | 3.124904 | ... | -4.585271 | -1.271175 | -3.837361 | 78.0 | F | disease tissue | stroke | 1 | 1 | 0.0 |
| 7 | 8047 | -4.051632 | -2.079779 | -1.550147 | 0.000000 | -4.402578 | -3.837361 | -0.343263 | -4.807960 | 3.284902 | ... | -4.322159 | -1.324576 | -4.051632 | 80.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 8 | 8048 | -3.076403 | -1.781880 | -1.456115 | 2.241551 | -3.837361 | -2.441207 | -0.285874 | -5.492861 | 3.201956 | ... | -4.247583 | -1.157883 | -4.322159 | 69.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
| 9 | 8049 | -2.664729 | -1.718460 | -1.392179 | 2.131436 | -3.701353 | -2.414368 | -0.056003 | -5.492861 | 3.543689 | ... | -3.659672 | -0.974195 | -3.886935 | 75.0 | M | disease tissue | stroke | 1 | 1 | 1.0 |
10 rows × 78 columns
统计和处理空缺数据
#统计甲基化数据
pd.isnull(MethylationData.iloc[:,:-7]).sum().sum()
0
#统计其他附加数据
pd.isnull(MethylationData.iloc[:,-7:]).sum()
age 0 gender 68 sample_type 0 disease 0 disease_encode 0 sample_type_encode 0 gender_encode 68 dtype: int64
from collections import Counter
print(Counter(MethylationData['gender']))
Counter({'F': 3387, 'M': 2958, nan: 68})
MethylationData['gender'].fillna('M',inplace=True)
MethylationData['gender_encode'].fillna('1',inplace=True)
MethylationData['gender'].hist()
<AxesSubplot:>
print(Counter(MethylationData['gender']))
Counter({'F': 3387, 'M': 3026})
pd.isnull(MethylationData).sum().sum()
0
提取算法可处理的数据进行机器学习分析,筛选特征甲基化位点
Methylation=MethylationData.iloc[:,1:-7] #甲基化数据
MapData=MethylationData.loc[:,['age','gender_encode','disease_encode']] #其他附加数据
MLData=pd.concat([Methylation,MapData],axis=1) #数据合并
MLData.shape
(6413, 73)
由于正常样本与卒中样本总量差异较大,因此使用数据重采样算法解决数据类别分布不均衡问题。 数据重采样策略针对训练集进行,外部测试集不使用数据重采样。同时,数据重采样后的训练集采用10-折交叉验证训练,独立外部测试集测试。
#统计和汇总疾病数据分布
print(Counter(MethylationData['disease']))
MethylationData['disease'].hist()
Counter({'control': 6266, 'stroke': 147})
<AxesSubplot:>
编写模型训练函数——常规训练
#查看模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Display_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def PR_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
predict=Model.predict(X_test) #获取预测标签
accuracy=accuracy_score(y_test,predict)
precision, recall, thresholds = precision_recall_curve(y_test, predict_score) #计算PR曲线
PR=DataFrame() #将PR曲线数据合并到DataFrame
#PR['thresholds']=thresholds
PR['recall']=recall
PR['precision']=precision
return PR,accuracy
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def ROC_Curve(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,train_size=0.7,random_state=2024) #划分数据集
predict_score=Model.predict_proba(X_test)[:, 1] #获取概率值
fpr, tpr, thresholds = roc_curve(y_test, predict_score)
roc_auc = auc(fpr, tpr) #计算AUC
ROC=DataFrame() #将PR曲线数据合并到DataFrame
#ROC['thresholds']=thresholds
ROC['tpr']=tpr
ROC['fpr']=fpr
return ROC,roc_auc
#编写模型训练评估函数
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTestModel(Model,Data,Label):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024) #划分数据集
Model.fit(X_train,y_train) #训练模型
TrainPredict=Model.predict(X_train) #计算训练集指标
TrainPrecision=precision_score(y_train,TrainPredict)
TrainRecall=recall_score(y_train,TrainPredict)
TrainF1=f1_score(y_train,TrainPredict)
TrainAcuracy=accuracy_score(y_train,TrainPredict)
print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(TrainPrecision,TrainRecall,TrainF1,TrainAcuracy))
TestPredict=Model.predict(X_test) #测试集预测结果
TestPrecision=precision_score(y_test,TestPredict)
TestRecall=recall_score(y_test,TestPredict)
TestF1=f1_score(y_test,TestPredict)
TestAccuracy=accuracy_score(y_test,TestPredict)
print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
print('-------------------测试集混淆举证-------------------')
Display_ConfusionMatrix(model=Model,data=X_test,target=y_test)
编写模型训练函数——数据重采样
#绘制模型混淆矩阵
from sklearn.metrics import confusion_matrix,ConfusionMatrixDisplay
def Computer_ConfusionMatrix(model,data,target):
ConfusionMatrix_result=confusion_matrix(target,model.predict(data),labels=[0,1]) #计算混淆举证
Display_ConfusionMatrix=ConfusionMatrixDisplay(ConfusionMatrix_result,display_labels=['control','Stroke'])
Display_ConfusionMatrix.plot(include_values=True, cmap='viridis', xticks_rotation='horizontal', values_format='d', ax=None)
plt.title('Confusion Matrix')
plt.show()
#计算PR曲线数据
from pandas import DataFrame
from sklearn.metrics import precision_recall_curve,accuracy_score
def Computer_PRCurve(Model,Data,Label):
predict_score=Model.predict_proba(Data)[:, 1] #获取概率值
predict=Model.predict(Data) #获取预测标签
accuracy=accuracy_score(Label,predict)
precision, recall, thresholds = precision_recall_curve(Label, predict_score) #计算PR曲线
PR=DataFrame() #将PR曲线数据合并到DataFrame
PR['recall']=recall
PR['precision']=precision
return PR,accuracy
#计算ROC曲线数据
from sklearn.metrics import roc_curve, auc
def Computer_ROCCurve(Model,Data,Label):
predict_score=Model.predict_proba(Data)[:, 1] #获取概率值
fpr, tpr, thresholds = roc_curve(Label, predict_score)
roc_auc = auc(fpr, tpr) #计算AUC
ROC=DataFrame() #将PR曲线数据合并到DataFrame
ROC['tpr']=tpr
ROC['fpr']=fpr
return ROC,roc_auc
#模型训练-评估函数
import numpy as np
from collections import Counter
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split,KFold
from sklearn.metrics import precision_score,recall_score,f1_score,accuracy_score
def RunTrainModelCV(Resmaple,Model,Data,Label,cv):
X_train,X_test,y_train,y_test=train_test_split(Data,Label,stratify=Label,train_size=0.7,random_state=2024) #划分数据集
X_train_Re,y_train_Re=Resmaple.fit_resample(X_train,y_train)
print('resample shape:',Counter(y_train_Re))
kf=KFold(n_splits=cv, shuffle=True, random_state=2025)
Precision_fold,Recall_fold,F1Score_fold,Accuracy_fold=[],[],[],[]
ValidPrecision_fold,ValidRecall_fold,ValidF1Score_fold,ValidAccuracy_fold=[],[],[],[]
for train_index,valid_index in kf.split(X_train_Re):
# 划分出当前折的训练和验证数据
X_fold_train, X_fold_val = X_train_Re.iloc[train_index,:], X_train_Re.iloc[valid_index,:]
y_fold_train, y_fold_val = y_train_Re.iloc[train_index], y_train_Re.iloc[valid_index]
Model.fit(X_fold_train,y_fold_train) #训练模型
# Train Score
TrainPredict_fold=Model.predict(X_fold_train) #计算训练集指标
TrainPrecision=precision_score(y_fold_train,TrainPredict_fold)
Precision_fold.append(TrainPrecision)
TrainRecall=recall_score(y_fold_train,TrainPredict_fold)
Recall_fold.append(TrainRecall)
TrainF1=f1_score(y_fold_train,TrainPredict_fold)
F1Score_fold.append(TrainF1)
TrainAcuracy=accuracy_score(y_fold_train,TrainPredict_fold)
Accuracy_fold.append(TrainAcuracy)
# Valid Score
ValidPredict_fold=Model.predict(X_fold_val) #计算训练集指标
ValidPrecision=precision_score(y_fold_val,ValidPredict_fold)
ValidPrecision_fold.append(ValidPrecision)
ValidRecall=recall_score(y_fold_val,ValidPredict_fold)
ValidRecall_fold.append(ValidRecall)
ValidF1=f1_score(y_fold_val,ValidPredict_fold)
ValidF1Score_fold.append(ValidF1)
ValidAcuracy=accuracy_score(y_fold_val,ValidPredict_fold)
ValidAccuracy_fold.append(ValidAcuracy)
print('Model train Precision of flod:',Precision_fold)
print('Model train Recall of flod:',Recall_fold)
print('Model train F1 Score of flod:',F1Score_fold)
print('Model train Accuracy of flod:',Accuracy_fold)
print('模型训练集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(Precision_fold),np.mean(Recall_fold),
np.mean(F1Score_fold),np.mean(Accuracy_fold)))
print('Model valid Precision of flod:',ValidPrecision_fold)
print('Model valid train Recall of flod:',ValidRecall_fold)
print('Model valid train F1 Score of flod:',ValidF1Score_fold)
print('Model valid train Accuracy of flod:',ValidAccuracy_fold)
print('模型Valid集Precision:{0},Recall:{1},F1_Score:{2},Accuracy:{3}'.format(np.mean(ValidPrecision_fold),np.mean(ValidRecall_fold),
np.mean(ValidF1Score_fold),np.mean(ValidAccuracy_fold)))
Model.fit(X_train_Re,y_train_Re)
TestPredict=Model.predict(X_test) #测试集预测结果
TestPrecision=precision_score(y_test,TestPredict)
TestRecall=recall_score(y_test,TestPredict)
TestF1=f1_score(y_test,TestPredict)
TestAccuracy=accuracy_score(y_test,TestPredict)
print('模型测试集Precision:{0},Recall:{1},F1_score:{2},Accuracy:{3}'.format(TestPrecision,TestRecall,TestF1,TestAccuracy))
print('-------------------测试集混淆举证-------------------')
Computer_ConfusionMatrix(model=Model,data=X_test,target=y_test)
PR,accuracy=Computer_PRCurve(Model=Model,Data=X_test,Label=y_test)
ROC,roc_auc=Computer_ROCCurve(Model=Model,Data=X_test,Label=y_test)
return PR,accuracy,ROC,roc_auc,Model
划分数据集并测试欠采样算法.
#切割数据
MLTestData=MLData.iloc[:,:-1]
MLTestLabel=MLData.iloc[:,-1]
#数据归一化
from pandas import DataFrame
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ColumnsNames=MLTestData.columns
Stand=StandardScaler()
StandData=Stand.fit_transform(MLTestData)
MinMax=MinMaxScaler(feature_range=(0,1))
MinMaxData=MinMax.fit_transform(StandData)
MLTestData=DataFrame(MinMaxData)
MLTestData.columns=ColumnsNames
#使用随机森林算法作为衡量基准测试数据重采样算法性能
from sklearn.ensemble import RandomForestClassifier
Forest=RandomForestClassifier(random_state=2024)
RunTestModel(Model=Forest,Data=MLTestData,Label=MLTestLabel)
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0 模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792 -------------------测试集混淆举证-------------------
#计算PR和ROC曲线
Forest_PR,Forest_Accuracy=PR_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_ROC,Forest_ROC_AUC=ROC_Curve(Model=Forest,Data=MLTestData,Label=MLTestLabel)
Forest_PR.to_excel('Data/PR Curve/Resample/Forest_PR.xlsx')
Forest_ROC.to_excel('Data/ROC Curve/Resample/Forest_ROC.xlsx')
#使用CNN算法进行欠采样
from imblearn.under_sampling import CondensedNearestNeighbour
CNN=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestCNN=RandomForestClassifier(random_state=2024)
ForestCNN_PR,accuracyCNN,ForestCNN_ROC,roc_auc_CNN,Forestcnn=RunTrainModelCV(Resmaple=CNN,Model=ForestCNN,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 0.8181818181818182, 0.8888888888888888, 1.0, 1.0, 1.0, 0.8, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.875, 1.0, 0.8888888888888888, 0.7272727272727273, 0.6363636363636364, 0.8181818181818182, 0.7272727272727273, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.8235294117647058, 0.9333333333333333, 0.9, 0.8888888888888888, 0.8421052631578948, 0.7777777777777778, 0.9, 0.761904761904762, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.896551724137931, 0.9655172413793104, 0.9310344827586207, 0.9285714285714286, 0.8928571428571429, 0.8571428571428571, 0.9285714285714286, 0.8214285714285714, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9507070707070706,Recall:0.7683585858585859,F1_Score:0.8406486805248414,Accuracy:0.8935960591133005
模型测试集Precision:0.782608695652174,Recall:0.8181818181818182,F1_score:0.8,Accuracy:0.9906444906444907
-------------------测试集混淆举证-------------------
#输出PR和ROC曲线数据
ForestCNN_PR.to_excel('Data/PR Curve/Resample/ForestCNN_PR.xlsx')
ForestCNN_ROC.to_excel('Data/ROC Curve/Resample/ForestCNN_ROC.xlsx')
#使用IHT算法进行欠采样
from imblearn.under_sampling import InstanceHardnessThreshold
IHT=InstanceHardnessThreshold(random_state=2024,cv=5,n_jobs=-1)
ForestIHT=RandomForestClassifier(random_state=2024)
ForestIHT_PR,accuracyIHT,ForestIHT_ROC,roc_auc_IHT,ForestIHT=RunTrainModelCV(Resmaple=IHT,Model=ForestIHT,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 2265, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.3333333333333333, 0.7142857142857143, 0.45454545454545453, 0.8333333333333334, 0.5, 0.375, 0.5384615384615384, 0.7142857142857143, 0.38461538461538464, 0.6363636363636364]
Model valid train F1 Score of flod: [0.5, 0.8333333333333333, 0.625, 0.9090909090909091, 0.6666666666666666, 0.5454545454545454, 0.7000000000000001, 0.8333333333333333, 0.5555555555555556, 0.7777777777777778]
Model valid train Accuracy of flod: [0.9831223628691983, 0.9915611814345991, 0.9746835443037974, 0.9915611814345991, 0.9831223628691983, 0.9789029535864979, 0.9746835443037974, 0.9831223628691983, 0.9661016949152542, 0.9830508474576272]
模型Valid集Precision:1.0,Recall:0.548422410922411,F1_Score:0.6946212121212121,Accuracy:0.9809912036043766
模型测试集Precision:0.7333333333333333,Recall:0.5,F1_score:0.5945945945945945,Accuracy:0.9844074844074844
-------------------测试集混淆举证-------------------
#计算PR和ROC曲线
ForestIHT_PR.to_excel('Data/PR Curve/Resample/ForestIHT_PR.xlsx')
ForestIHT_ROC.to_excel('Data/ROC Curve/Resample/ForestIHT_ROC.xlsx')
#使用NearMiss进行欠采样
from imblearn.under_sampling import NearMiss
NM=NearMiss(sampling_strategy='not minority',n_jobs=-1)
ForestNM=RandomForestClassifier(random_state=2024)
ForestNM_PR,accuracyNM,ForestNM_ROC,roc_auc_NM,ForestNM=RunTrainModelCV(Resmaple=NM,Model=ForestNM,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [1.0, 1.0, 1.0, 0.9285714285714286, 1.0, 0.9, 1.0, 1.0, 1.0, 1.0]
Model valid train Recall of flod: [0.8888888888888888, 1.0, 0.9333333333333333, 0.9285714285714286, 0.75, 1.0, 1.0, 1.0, 1.0, 1.0]
Model valid train F1 Score of flod: [0.9411764705882353, 1.0, 0.9655172413793104, 0.9285714285714286, 0.8571428571428571, 0.9473684210526316, 1.0, 1.0, 1.0, 1.0]
Model valid train Accuracy of flod: [0.9523809523809523, 1.0, 0.9523809523809523, 0.9047619047619048, 0.9047619047619048, 0.9523809523809523, 1.0, 1.0, 1.0, 1.0]
模型Valid集Precision:0.9828571428571429,Recall:0.950079365079365,F1_Score:0.9639776418734464,Accuracy:0.9666666666666666
模型测试集Precision:0.037374658158614404,Recall:0.9318181818181818,F1_score:0.07186678352322523,Accuracy:0.4495841995841996
-------------------测试集混淆举证-------------------
#计算PR和ROC曲线
ForestNM_PR.to_excel('Data/PR Curve/Resample/ForestNM_PR.xlsx')
ForestNM_ROC.to_excel('Data/ROC Curve/Resample/ForestNM_ROC.xlsx')
#使用NeighbourhoodCleaningRule进行欠采样
from imblearn.under_sampling import NeighbourhoodCleaningRule
NBC=NeighbourhoodCleaningRule(sampling_strategy='not minority',n_jobs=-1)
ForestNBC=RandomForestClassifier(random_state=2024)
ForestNBC_PR,accuracyNBC,ForestNBC_ROC,roc_auc_NBC,ForestNBC=RunTrainModelCV(Resmaple=NBC,Model=ForestNBC,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 4325, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 0.0, 0.0, 1.0, 0.0, 1.0, 1.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.16666666666666666, 0.0, 0.0, 0.2222222222222222, 0.0, 0.0625, 0.09090909090909091, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.2857142857142857, 0.0, 0.0, 0.3636363636363636, 0.0, 0.11764705882352941, 0.16666666666666669, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9729119638826185, 0.9887133182844243, 0.9661399548532731, 0.981941309255079, 0.9841986455981941, 0.9796839729119639, 0.9661399548532731, 0.9774266365688488, 0.9796380090497737, 0.9819004524886877]
模型Valid集Precision:0.4,Recall:0.054229797979797975,F1_Score:0.09336643748408455,Accuracy:0.9778694217746138
模型测试集Precision:1.0,Recall:0.09090909090909091,F1_score:0.16666666666666669,Accuracy:0.9792099792099792
-------------------测试集混淆举证-------------------
#计算PR和ROC曲线
ForestNBC_PR.to_excel('Data/PR Curve/Resample/ForestNBC_PR.xlsx')
ForestNBC_ROC.to_excel('Data/ROC Curve/Resample/ForestNBC_ROC.xlsx')
#使用OneSidedSelection进行欠采样
from imblearn.under_sampling import OneSidedSelection
OSS=OneSidedSelection(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
ForestOSS=RandomForestClassifier(random_state=2024)
ForestOSS_PR,accuracyOSS,ForestOSS_ROC,roc_auc_OSS,ForestOSS=RunTrainModelCV(Resmaple=OSS,Model=ForestOSS,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 4345, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.0, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Recall of flod: [0.0, 0.25, 0.13333333333333333, 0.15384615384615385, 0.15384615384615385, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train F1 Score of flod: [0.0, 0.4, 0.23529411764705882, 0.2666666666666667, 0.2666666666666667, 0.0, 0.0, 0.0, 0.0, 0.0]
Model valid train Accuracy of flod: [0.9752808988764045, 0.9865168539325843, 0.9707865168539326, 0.9752808988764045, 0.9752808988764045, 0.9887640449438202, 0.9775280898876404, 0.9730337078651685, 0.9864864864864865, 0.9774774774774775]
模型Valid集Precision:0.4,Recall:0.0691025641025641,F1_Score:0.11686274509803922,Accuracy:0.9786435874076321
模型测试集Precision:1.0,Recall:0.06818181818181818,F1_score:0.1276595744680851,Accuracy:0.9786902286902287
-------------------测试集混淆举证-------------------
#计算PR和ROC曲线
ForestOSS_PR.to_excel('Data/PR Curve/Resample/ForestOSS_PR.xlsx')
ForestOSS_ROC.to_excel('Data/ROC Curve/Resample/ForestOSS_ROC.xlsx')
#使用随机欠采样进行数据欠采样
from imblearn.under_sampling import RandomUnderSampler
RUS=RandomUnderSampler(sampling_strategy='not minority',random_state=2024)
ForestRUS=RandomForestClassifier(random_state=2024)
ForestRUS_PR,accuracyRUS,ForestRUS_ROC,roc_auc_RUS,ForestRUS=RunTrainModelCV(Resmaple=RUS,Model=ForestRUS,Data=MLTestData,Label=MLTestLabel,cv=10)
resample shape: Counter({0: 103, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.9, 0.8181818181818182, 1.0, 1.0, 0.8, 1.0, 1.0, 0.9, 1.0, 1.0]
Model valid train Recall of flod: [1.0, 0.9, 0.9333333333333333, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.8888888888888888]
Model valid train F1 Score of flod: [0.9473684210526316, 0.8571428571428572, 0.9655172413793104, 1.0, 0.888888888888889, 1.0, 1.0, 0.9473684210526316, 1.0, 0.9411764705882353]
Model valid train Accuracy of flod: [0.9523809523809523, 0.8571428571428571, 0.9523809523809523, 1.0, 0.9047619047619048, 1.0, 1.0, 0.95, 1.0, 0.95]
模型Valid集Precision:0.9418181818181818,Recall:0.9722222222222221,F1_Score:0.9547462300104556,Accuracy:0.9566666666666667
模型测试集Precision:0.2679738562091503,Recall:0.9318181818181818,F1_score:0.416243654822335,Accuracy:0.9402286902286903
-------------------测试集混淆举证-------------------
#计算PR和ROC曲线
ForestRUS_PR.to_excel('Data/PR Curve/Resample/ForestRUS_PR.xlsx')
ForestRUS_ROC.to_excel('Data/ROC Curve/Resample/ForestRUS_ROC.xlsx')
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(Forest_PR['recall'], Forest_PR['precision'],color='red',label='RandomForest=%f'%Forest_Accuracy)
PR_ax.plot(ForestCNN_PR['recall'], ForestCNN_PR['precision'],color='green',label='CNN=%f'%accuracyCNN)
PR_ax.plot(ForestIHT_PR['recall'], ForestIHT_PR['precision'],color='blue',label='IHT=%f'%accuracyIHT)
PR_ax.plot(ForestNBC_PR['recall'], ForestNBC_PR['precision'],color='yellow',label='NBC=%f'%accuracyNBC)
PR_ax.plot(ForestNM_PR['recall'], ForestNM_PR['precision'],color='purple',label='NM=%f'%accuracyNM)
PR_ax.plot(ForestOSS_PR['recall'], ForestOSS_PR['precision'],color='cyan',label='OSS=%f'%accuracyOSS)
PR_ax.plot(ForestRUS_PR['recall'],ForestRUS_PR['precision'],color='pink',label='RUS=%f'%accuracyRUS)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')
PR_ax.set_ylabel('Precision')
plt.legend(loc="best")
plt.show()
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(Forest_ROC['fpr'], Forest_ROC['tpr'],color='red',label='RandomForest=%f'%Forest_ROC_AUC)
ROC_ax.plot(ForestCNN_ROC['fpr'], ForestCNN_ROC['tpr'],color='green',label='CNN=%f'%roc_auc_CNN)
ROC_ax.plot(ForestIHT_ROC['fpr'], ForestIHT_ROC['tpr'],color='blue',label='IHT=%f'%roc_auc_IHT)
ROC_ax.plot(ForestNBC_ROC['fpr'], ForestNBC_ROC['tpr'],color='yellow',label='NBC=%f'%roc_auc_NBC)
ROC_ax.plot(ForestNM_ROC['fpr'], ForestNM_ROC['tpr'],color='purple',label='NM=%f'%roc_auc_NM)
ROC_ax.plot(ForestOSS_ROC['fpr'], ForestOSS_ROC['tpr'],color='cyan',label='OSS=%f'%roc_auc_OSS)
ROC_ax.plot(ForestRUS_ROC['fpr'],ForestRUS_ROC['tpr'],color='pink',label='RUS=%f'%roc_auc_RUS)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])
ROC_ax.set_ylim([0, 1.05])
ROC_ax.set_xlabel('FPR')
ROC_ax.set_ylabel('TPR')
plt.legend(loc="best")
plt.show()
import joblib
Model=[Forest,ForestCNN,ForestIHT,ForestNM,ForestNBC,ForestOSS,ForestRUS]
ModelStr=['Forest.pkl','ForestCNN.pkl','ForestIHT.pkl','ForestNM.pkl','ForestNBC.pkl','ForestOSS.pkl','ForestRUS.pkl']
try:
for model,modelstr in zip(Model,ModelStr):
joblib.dump(model,'ML Models/Resample/'+modelstr)
print('模型保存成功!')
except:
print('模型保存异常!!!')
模型保存成功!
选择CNN作为数据重采样算法
ReData=MLData.iloc[:,:-1] #提取数据集
ReLabel=MLData.iloc[:,-1] #提取数据标签
print(ReData.shape)
print(ReLabel.shape)
(6413, 72) (6413,)
预处理数据:数据缩放、描述性统计学
from sklearn.preprocessing import StandardScaler,MinMaxScaler
ReMLData_name=ReData.columns
MLStand=StandardScaler() #数据标准化
StandData=MLStand.fit_transform(ReData)
MinMaxData=MinMaxScaler(feature_range=(0,1)) #数据缩放
MinMaxdata=MinMaxData.fit_transform(StandData)
MLRunData=pd.DataFrame(MinMaxdata)
MLRunData.columns=ReMLData_name
MLRunData.head(10)
| cg03706273 | cg00412368 | cg01370179 | cg01522249 | cg01999212 | cg03718079 | cg03977822 | cg05100261 | cg05876899 | cg06104510 | ... | cg00941229 | cg00958409 | cg00962755 | cg01077609 | cg01101221 | cg01446576 | cg01550055 | cg01717973 | age | gender_encode | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | 0.366703 | 0.699228 | 0.627539 | 0.339860 | 0.725793 | 0.719028 | 0.610471 | 0.692839 | 0.495571 | 0.842822 | ... | 0.725793 | 0.715924 | 0.372852 | 0.399866 | 0.645909 | 0.720955 | 0.598192 | 0.541884 | 0.684211 | 0.0 |
| 1 | 0.463465 | 0.592648 | 0.638988 | 0.312063 | 0.502166 | 0.564485 | 0.531935 | 0.435958 | 0.520210 | 0.606743 | ... | 0.583368 | 0.699491 | 0.360245 | 0.375090 | 0.543283 | 0.593423 | 0.671892 | 0.468489 | 0.456140 | 1.0 |
| 2 | 0.419803 | 0.546794 | 0.629799 | 0.435326 | 0.553452 | 0.589086 | 0.544620 | 0.353111 | 0.507977 | 0.627347 | ... | 0.522001 | 0.742118 | 0.410469 | 0.375090 | 0.603949 | 0.560104 | 0.608857 | 0.346457 | 0.807018 | 1.0 |
| 3 | 0.316703 | 0.674653 | 0.606416 | 0.256460 | 0.675869 | 0.637623 | 0.701250 | 0.730939 | 0.531910 | 0.750907 | ... | 0.566385 | 0.722588 | 0.310634 | 0.399866 | 0.567367 | 0.502166 | 0.560468 | 0.442870 | 0.763158 | 0.0 |
| 4 | 0.345865 | 0.506133 | 0.618676 | 0.441581 | 0.619161 | 0.521338 | 0.609608 | 0.410436 | 0.520210 | 0.644800 | ... | 0.583368 | 0.740716 | 0.404357 | 0.349814 | 0.549814 | 0.538830 | 0.567359 | 0.459086 | 0.763158 | 1.0 |
| 5 | 0.435973 | 0.561169 | 0.624070 | 0.419851 | 0.703336 | 0.519129 | 0.620814 | 0.421574 | 0.527038 | 0.624162 | ... | 0.477988 | 0.757873 | 0.433043 | 0.382025 | 0.543283 | 0.553452 | 0.670629 | 0.448602 | 0.359649 | 1.0 |
| 6 | 0.402192 | 0.691920 | 0.663395 | 0.306385 | 0.693490 | 0.621787 | 0.672487 | 0.760064 | 0.511868 | 0.839015 | ... | 0.553452 | 0.705214 | 0.350654 | 0.295393 | 0.481720 | 0.502166 | 0.666816 | 0.472852 | 0.684211 | 0.0 |
| 7 | 0.371111 | 0.542279 | 0.628108 | 0.312063 | 0.522001 | 0.462208 | 0.563362 | 0.364092 | 0.524701 | 0.624162 | ... | 0.619161 | 0.701428 | 0.465032 | 0.359144 | 0.582520 | 0.530733 | 0.655144 | 0.453995 | 0.701754 | 1.0 |
| 8 | 0.441267 | 0.564934 | 0.635818 | 0.479486 | 0.583368 | 0.582309 | 0.575699 | 0.307450 | 0.518048 | 0.590573 | ... | 0.615253 | 0.712455 | 0.357063 | 0.388415 | 0.595861 | 0.538830 | 0.691576 | 0.430188 | 0.605263 | 1.0 |
| 9 | 0.470881 | 0.569757 | 0.641060 | 0.471262 | 0.598135 | 0.584618 | 0.625118 | 0.307450 | 0.545459 | 0.560855 | ... | 0.649430 | 0.724201 | 0.348719 | 0.382025 | 0.561819 | 0.602660 | 0.731723 | 0.468489 | 0.657895 | 1.0 |
10 rows × 72 columns
使用CNN算法进行预处理
from imblearn.under_sampling import CondensedNearestNeighbour
CNNML=CondensedNearestNeighbour(sampling_strategy='not minority',random_state=2024,n_jobs=-1)
#测试Logistic回归
from sklearn.linear_model import LogisticRegression
Logistic=LogisticRegression(penalty="l2",dual=True,tol=1e-4,C=1.0,fit_intercept=True,random_state=2024,solver='liblinear',
max_iter=100,multi_class="auto",verbose=0, warm_start=False,n_jobs=-1,l1_ratio=None)
Logistic_PR,Logistic_Accuracy,Logistic_ROC,Logistic_AUC,Logistic_ML=RunTrainModelCV(Resmaple=CNNML,Model=Logistic,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9365079365079365, 0.8970588235294118, 0.9130434782608695, 0.9130434782608695, 0.921875, 0.9333333333333333, 0.9508196721311475, 0.9230769230769231, 0.9344262295081968, 0.9142857142857143]
Model train Recall of flod: [0.6344086021505376, 0.6421052631578947, 0.6702127659574468, 0.6702127659574468, 0.6413043478260869, 0.6086956521739131, 0.6304347826086957, 0.6521739130434783, 0.6263736263736264, 0.6956521739130435]
Model train F1 Score of flod: [0.7564102564102564, 0.7484662576687117, 0.7730061349693251, 0.7730061349693251, 0.7564102564102563, 0.7368421052631579, 0.758169934640523, 0.7643312101910829, 0.7500000000000001, 0.7901234567901234]
Model train Accuracy of flod: [0.8503937007874016, 0.8385826771653543, 0.8543307086614174, 0.8549019607843137, 0.8509803921568627, 0.8431372549019608, 0.8549019607843137, 0.8549019607843137, 0.8509803921568627, 0.8666666666666667]
模型训练集Precision:0.9237470588894403,Recall:0.6471573893162169,F1_Score:0.7606765747312763,Accuracy:0.8519777674849467
Model valid Precision of flod: [1.0, 0.8888888888888888, 0.7142857142857143, 1.0, 0.8333333333333334, 1.0, 0.7777777777777778, 0.8888888888888888, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.5555555555555556, 0.6666666666666666, 0.45454545454545453, 0.7272727272727273, 0.6363636363636364, 0.7272727272727273, 0.5, 0.45454545454545453]
Model valid train F1 Score of flod: [0.5714285714285715, 0.9411764705882353, 0.6250000000000001, 0.8, 0.5882352941176471, 0.8421052631578948, 0.7000000000000001, 0.7999999999999999, 0.6666666666666666, 0.625]
Model valid train Accuracy of flod: [0.7931034482758621, 0.9655172413793104, 0.7931034482758621, 0.8928571428571429, 0.75, 0.8928571428571429, 0.7857142857142857, 0.8571428571428571, 0.7857142857142857, 0.7857142857142857]
模型Valid集Precision:0.9103174603174603,Recall:0.6122222222222222,F1_Score:0.7159612265959016,Accuracy:0.8301724137931034
模型测试集Precision:0.8064516129032258,Recall:0.5681818181818182,F1_score:0.6666666666666667,Accuracy:0.987006237006237
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
Logistic_PR.to_excel('Data/PR Curve/ML/Logistic_PR.xlsx','UTF-8')
Logistic_ROC.to_excel('Data/ROC Curve/ML/Logistic_ROC.xlsx','UTF-8')
#测试SVM算法
from sklearn.svm import SVC
SVM_Classifier=SVC(C=1.0,kernel="rbf",degree=3,gamma="scale",coef0=0.0,shrinking=True,probability=True,tol=1e-3,
cache_size=200,max_iter=-1, decision_function_shape="ovr",random_state=2024)
SVM_PR,SVM_Accuracy,SVM_ROC,SVM_AUC,SVM_ML=RunTrainModelCV(Resmaple=CNNML,Model=SVM_Classifier,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9473684210526315, 0.9487179487179487, 0.9285714285714286, 0.9302325581395349, 0.927710843373494, 0.935064935064935, 0.9466666666666667, 0.9397590361445783, 0.9333333333333333, 0.9156626506024096]
Model train Recall of flod: [0.7741935483870968, 0.7789473684210526, 0.8297872340425532, 0.851063829787234, 0.8369565217391305, 0.782608695652174, 0.7717391304347826, 0.8478260869565217, 0.7692307692307693, 0.8260869565217391]
Model train F1 Score of flod: [0.8520710059171597, 0.8554913294797688, 0.8764044943820225, 0.888888888888889, 0.8800000000000001, 0.8520710059171598, 0.8502994011976047, 0.8914285714285715, 0.8433734939759036, 0.8685714285714285]
Model train Accuracy of flod: [0.9015748031496063, 0.9015748031496063, 0.9133858267716536, 0.9215686274509803, 0.9176470588235294, 0.9019607843137255, 0.9019607843137255, 0.9254901960784314, 0.8980392156862745, 0.9098039215686274]
模型训练集Precision:0.9353087821666962,Recall:0.8068440141173052,F1_Score:0.8658599619758508,Accuracy:0.9093006021306161
Model valid Precision of flod: [0.8, 0.8888888888888888, 0.875, 0.7777777777777778, 1.0, 1.0, 0.9, 0.8181818181818182, 1.0, 1.0]
Model valid train Recall of flod: [0.4, 1.0, 0.7777777777777778, 0.7777777777777778, 0.5454545454545454, 0.6363636363636364, 0.8181818181818182, 0.8181818181818182, 0.5833333333333334, 0.7272727272727273]
Model valid train F1 Score of flod: [0.5333333333333333, 0.9411764705882353, 0.823529411764706, 0.7777777777777778, 0.7058823529411764, 0.7777777777777778, 0.8571428571428572, 0.8181818181818182, 0.7368421052631579, 0.8421052631578948]
Model valid train Accuracy of flod: [0.7586206896551724, 0.9655172413793104, 0.896551724137931, 0.8571428571428571, 0.8214285714285714, 0.8571428571428571, 0.8928571428571429, 0.8571428571428571, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.9059848484848485,Recall:0.7084343434343434,F1_Score:0.7813749167928734,Accuracy:0.8620689655172413
模型测试集Precision:0.85,Recall:0.7727272727272727,F1_score:0.8095238095238095,Accuracy:0.9916839916839917
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
SVM_PR.to_excel('Data/PR Curve/ML/SVM_PR.xlsx','UTF-8')
SVM_ROC.to_excel('Data/ROC Curve/ML/SVM_ROC.xlsx','UTF-8')
#测试决策树算法
import numpy as np
from sklearn.tree import DecisionTreeClassifier
DecisionTree=DecisionTreeClassifier(criterion='entropy',splitter='best',max_depth=7,min_samples_split=6,random_state=2024)
DTC_PR,DTC_Accuracy,DTC_ROC,DTC_AUC,DTC_ML=RunTrainModelCV(Resmaple=CNNML,Model=DecisionTree,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.989010989010989, 0.92, 0.9891304347826086, 0.9893617021276596, 0.9578947368421052, 0.9479166666666666, 0.9278350515463918, 0.989247311827957, 0.9574468085106383, 0.9574468085106383]
Model train Recall of flod: [0.967741935483871, 0.968421052631579, 0.9680851063829787, 0.9893617021276596, 0.9891304347826086, 0.9891304347826086, 0.9782608695652174, 1.0, 0.989010989010989, 0.9782608695652174]
Model train F1 Score of flod: [0.9782608695652174, 0.9435897435897437, 0.9784946236559139, 0.9893617021276596, 0.9732620320855614, 0.9680851063829786, 0.9523809523809524, 0.9945945945945946, 0.972972972972973, 0.967741935483871]
Model train Accuracy of flod: [0.984251968503937, 0.9566929133858267, 0.984251968503937, 0.9921568627450981, 0.9803921568627451, 0.9764705882352941, 0.9647058823529412, 0.996078431372549, 0.9803921568627451, 0.9764705882352941]
模型训练集Precision:0.9625290509825655,Recall:0.9817403394332729,F1_Score:0.9718744532839466,Accuracy:0.9791863517060367
Model valid Precision of flod: [0.8571428571428571, 0.6, 0.6363636363636364, 0.5, 0.8888888888888888, 0.8888888888888888, 0.75, 0.7272727272727273, 0.8888888888888888, 0.7272727272727273]
Model valid train Recall of flod: [0.6, 0.75, 0.7777777777777778, 0.4444444444444444, 0.7272727272727273, 0.7272727272727273, 0.5454545454545454, 0.7272727272727273, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.6666666666666665, 0.7000000000000001, 0.47058823529411764, 0.7999999999999999, 0.7999999999999999, 0.631578947368421, 0.7272727272727273, 0.761904761904762, 0.7272727272727273]
Model valid train Accuracy of flod: [0.8275862068965517, 0.7931034482758621, 0.7931034482758621, 0.6785714285714286, 0.8571428571428571, 0.8571428571428571, 0.75, 0.7857142857142857, 0.8214285714285714, 0.7857142857142857]
模型Valid集Precision:0.7464718614718614,Recall:0.6693434343434344,F1_Score:0.6991166418720598,Accuracy:0.7949507389162561
模型测试集Precision:0.1336206896551724,Recall:0.7045454545454546,F1_score:0.2246376811594203,Accuracy:0.8887733887733887
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
DTC_PR.to_excel('Data/PR Curve/ML/DTC_PR.xlsx','UTF-8')
DTC_ROC.to_excel('Data/ROC Curve/ML/DTC_ROC.xlsx','UTF-8')
#对随机森林进行网格搜索
from sklearn.ensemble import RandomForestClassifier
RandomForest=RandomForestClassifier(n_estimators=27,criterion='entropy',max_depth=7,min_samples_split=6,
bootstrap=True,min_samples_leaf=9,max_features='sqrt',random_state=2024)
RondomForest_PR,RondomForest_Accuracy,RondomForest_ROC,RondomForest_AUC,Forest_ML=RunTrainModelCV(Resmaple=CNNML,Model=RandomForest,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [0.9887640449438202, 1.0, 0.9886363636363636, 1.0, 0.9886363636363636, 1.0, 1.0, 0.967032967032967, 1.0, 0.9882352941176471]
Model train Recall of flod: [0.946236559139785, 0.9368421052631579, 0.925531914893617, 0.9787234042553191, 0.9456521739130435, 0.967391304347826, 0.9347826086956522, 0.9565217391304348, 0.9560439560439561, 0.9130434782608695]
Model train F1 Score of flod: [0.967032967032967, 0.967391304347826, 0.9560439560439561, 0.989247311827957, 0.9666666666666666, 0.9834254143646408, 0.9662921348314606, 0.9617486338797815, 0.9775280898876404, 0.9491525423728814]
Model train Accuracy of flod: [0.9763779527559056, 0.9763779527559056, 0.968503937007874, 0.9921568627450981, 0.9764705882352941, 0.9882352941176471, 0.9764705882352941, 0.9725490196078431, 0.984313725490196, 0.9647058823529412]
模型训练集Precision:0.9921305033367162,Recall:0.9460769243943661,F1_Score:0.9684529021255777,Accuracy:0.9776161803304
Model valid Precision of flod: [1.0, 0.75, 0.7272727272727273, 0.9, 1.0, 1.0, 1.0, 0.8571428571428571, 1.0, 1.0]
Model valid train Recall of flod: [0.7, 0.75, 0.8888888888888888, 1.0, 0.5454545454545454, 0.5454545454545454, 0.8181818181818182, 0.5454545454545454, 0.6666666666666666, 0.6363636363636364]
Model valid train F1 Score of flod: [0.8235294117647058, 0.75, 0.7999999999999999, 0.9473684210526316, 0.7058823529411764, 0.7058823529411764, 0.9, 0.6666666666666665, 0.8, 0.7777777777777778]
Model valid train Accuracy of flod: [0.896551724137931, 0.8620689655172413, 0.8620689655172413, 0.9642857142857143, 0.8214285714285714, 0.8214285714285714, 0.9285714285714286, 0.7857142857142857, 0.8571428571428571, 0.8571428571428571]
模型Valid集Precision:0.9234415584415585,Recall:0.7096464646464647,F1_Score:0.7877106983144134,Accuracy:0.8656403940886699
模型测试集Precision:0.6415094339622641,Recall:0.7727272727272727,F1_score:0.7010309278350515,Accuracy:0.9849272349272349
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
RondomForest_PR.to_excel('Data/PR Curve/ML/RondomForest_PR.xlsx','UTF-8')
RondomForest_ROC.to_excel('Data/ROC Curve/ML/RondomForest_ROC.xlsx','UTF-8')
#测试XGBoost模型
from xgboost import XGBClassifier
XGBoost=XGBClassifier(n_jobs=-1,verbosity=1,tree_method='auto',gpu_id=0,random_state=2024)
XGBoost_PR,XGBoost_Accuracy,XGBoost_ROC,XGBoost_AUC,XGBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=XGBoost,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.8571428571428571, 0.7, 0.75, 0.8, 0.9, 1.0, 1.0, 0.6923076923076923, 0.8888888888888888, 0.8]
Model valid train Recall of flod: [0.6, 0.875, 1.0, 0.8888888888888888, 0.8181818181818182, 0.7272727272727273, 0.7272727272727273, 0.8181818181818182, 0.6666666666666666, 0.7272727272727273]
Model valid train F1 Score of flod: [0.7058823529411764, 0.7777777777777777, 0.8571428571428571, 0.8421052631578948, 0.8571428571428572, 0.8421052631578948, 0.8421052631578948, 0.7500000000000001, 0.761904761904762, 0.761904761904762]
Model valid train Accuracy of flod: [0.8275862068965517, 0.8620689655172413, 0.896551724137931, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.8928571428571429, 0.7857142857142857, 0.8214285714285714, 0.8214285714285714]
模型Valid集Precision:0.8388339438339438,Recall:0.7848737373737374,F1_Score:0.7998071158287876,Accuracy:0.8586206896551725
模型测试集Precision:0.42528735632183906,Recall:0.8409090909090909,F1_score:0.564885496183206,Accuracy:0.9703742203742204
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
XGBoost_PR.to_excel('Data/PR Curve/ML/XGBoost_PR.xlsx','UTF-8')
XGBoost_ROC.to_excel('Data/ROC Curve/ML/XGBoost_ROC.xlsx','UTF-8')
#测试LightGBM算法
from lightgbm import LGBMClassifier
LightGBM=LGBMClassifier(boosting_type='gbdt',num_leaves=31,learning_rate=0.01,n_estimators=200,
n_jobs=-1,objective='binary',metric='binary_logloss',keep_training_booster=True,
importance_type='gini',random_state=2024)
LightGBM_PR,LightGBM_Accuracy,LightGBM_ROC,LightGBM_AUC,LightGBM_ML=RunTrainModelCV(Resmaple=CNNML,Model=LightGBM,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 93, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000407 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.366142 -> initscore=-0.548805
[LightGBM] [Info] Start training from score -0.548805
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 95, number of negative: 159
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4236
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.374016 -> initscore=-0.515027
[LightGBM] [Info] Start training from score -0.515027
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 160
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000404 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4256
[LightGBM] [Info] Number of data points in the train set: 254, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.370079 -> initscore=-0.531879
[LightGBM] [Info] Start training from score -0.531879
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 94, number of negative: 161
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000497 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4268
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.368627 -> initscore=-0.538110
[LightGBM] [Info] Start training from score -0.538110
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000448 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4303
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000439 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4298
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000415 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4245
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000440 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4269
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 91, number of negative: 164
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000465 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4277
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.356863 -> initscore=-0.589007
[LightGBM] [Info] Start training from score -0.589007
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 92, number of negative: 163
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000431 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4292
[LightGBM] [Info] Number of data points in the train set: 255, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.360784 -> initscore=-0.571962
[LightGBM] [Info] Start training from score -0.571962
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
Model train Precision of flod: [1.0, 1.0, 1.0, 0.9893617021276596, 0.989010989010989, 1.0, 0.9888888888888889, 1.0, 1.0, 1.0]
Model train Recall of flod: [0.978494623655914, 0.9894736842105263, 1.0, 0.9893617021276596, 0.9782608695652174, 1.0, 0.967391304347826, 0.9891304347826086, 0.978021978021978, 0.9891304347826086]
Model train F1 Score of flod: [0.9891304347826088, 0.9947089947089947, 1.0, 0.9893617021276596, 0.9836065573770493, 1.0, 0.978021978021978, 0.994535519125683, 0.9888888888888888, 0.994535519125683]
Model train Accuracy of flod: [0.9921259842519685, 0.9960629921259843, 1.0, 0.9921568627450981, 0.9882352941176471, 1.0, 0.984313725490196, 0.996078431372549, 0.9921568627450981, 0.996078431372549]
模型训练集Precision:0.9967261580027538,Recall:0.9859265031494339,F1_Score:0.9912789594158546,Accuracy:0.9937208584221089
Model valid Precision of flod: [1.0, 0.7777777777777778, 0.8, 0.6, 0.875, 1.0, 1.0, 0.7, 0.8888888888888888, 0.8333333333333334]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.6666666666666666, 0.6363636363636364, 0.7272727272727273, 0.7272727272727273, 0.6363636363636364, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.8235294117647058, 0.823529411764706, 0.8421052631578948, 0.631578947368421, 0.7368421052631579, 0.8421052631578948, 0.8421052631578948, 0.6666666666666666, 0.761904761904762, 0.8695652173913043]
Model valid train Accuracy of flod: [0.896551724137931, 0.896551724137931, 0.896551724137931, 0.75, 0.8214285714285714, 0.8928571428571429, 0.8928571428571429, 0.75, 0.8214285714285714, 0.8928571428571429]
模型Valid集Precision:0.8475000000000001,Recall:0.7433585858585859,F1_Score:0.7839932311597407,Accuracy:0.8511083743842365
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Info] Number of positive: 103, number of negative: 180
[LightGBM] [Info] Auto-choosing col-wise multi-threading, the overhead of testing was 0.000492 seconds.
You can set `force_col_wise=true` to remove the overhead.
[LightGBM] [Info] Total Bins 4619
[LightGBM] [Info] Number of data points in the train set: 283, number of used features: 72
[LightGBM] [Info] [binary:BoostFromScore]: pavg=0.363958 -> initscore=-0.558228
[LightGBM] [Info] Start training from score -0.558228
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
[LightGBM] [Warning] No further splits with positive gain, best gain: -inf
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[LightGBM] [Warning] Unknown parameter: keep_training_booster
模型测试集Precision:0.47297297297297297,Recall:0.7954545454545454,F1_score:0.5932203389830508,Accuracy:0.975051975051975
-------------------测试集混淆举证-------------------
[LightGBM] [Warning] Unknown parameter: keep_training_booster
[LightGBM] [Warning] Unknown parameter: keep_training_booster [LightGBM] [Warning] Unknown parameter: keep_training_booster [LightGBM] [Warning] Unknown parameter: keep_training_booster
#输出PR曲线和ROC曲线结果
LightGBM_PR.to_excel('Data/PR Curve/ML/LightGBM_PR.xlsx','UTF-8')
LightGBM_ROC.to_excel('Data/ROC Curve/ML/LightGBM_ROC.xlsx','UTF-8')
#测试CatBoost算法
from catboost import CatBoostClassifier
CatBoost=CatBoostClassifier(learning_rate=0.01,depth=6,iterations=500,thread_count=-1)
CatBoost_PR,CatBoost_Accuracy,CatBoost_ROC,CatBoost_AUC,CatBoost_ML=RunTrainModelCV(Resmaple=CNNML,Model=CatBoost,Data=MLRunData,Label=ReLabel,cv=10)
resample shape: Counter({0: 180, 1: 103})
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Model train Precision of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Recall of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train F1 Score of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
Model train Accuracy of flod: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
模型训练集Precision:1.0,Recall:1.0,F1_Score:1.0,Accuracy:1.0
Model valid Precision of flod: [0.875, 0.7777777777777778, 0.8888888888888888, 0.8888888888888888, 1.0, 1.0, 1.0, 0.7692307692307693, 1.0, 0.9090909090909091]
Model valid train Recall of flod: [0.7, 0.875, 0.8888888888888888, 0.8888888888888888, 0.8181818181818182, 0.8181818181818182, 0.8181818181818182, 0.9090909090909091, 0.6666666666666666, 0.9090909090909091]
Model valid train F1 Score of flod: [0.7777777777777777, 0.823529411764706, 0.8888888888888888, 0.8888888888888888, 0.9, 0.9, 0.9, 0.8333333333333333, 0.8, 0.9090909090909091]
Model valid train Accuracy of flod: [0.8620689655172413, 0.896551724137931, 0.9310344827586207, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.9285714285714286, 0.8571428571428571, 0.8571428571428571, 0.9285714285714286]
模型Valid集Precision:0.9108877233877232,Recall:0.8292171717171717,F1_Score:0.8621509209744505,Accuracy:0.9046798029556651
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模型测试集Precision:0.6333333333333333,Recall:0.8636363636363636,F1_score:0.7307692307692307,Accuracy:0.9854469854469855
-------------------测试集混淆举证-------------------
#输出PR曲线和ROC曲线结果
CatBoost_PR.to_excel('Data/PR Curve/ML/CatBoost_PR.xlsx','UTF-8')
CatBoost_ROC.to_excel('Data/ROC Curve/ML/CatBoost_ROC.xlsx','UTF-8')
#绘制PR曲线
import matplotlib.pyplot as plt
PR_curve=plt.figure(dpi=300)
PR_ax=PR_curve.add_subplot(111)
PR_ax.set_title('Precision-Recall curve')
PR_ax.plot(CatBoost_PR['recall'], CatBoost_PR['precision'],color='red',label='CatBoost=%f'%CatBoost_Accuracy)
PR_ax.plot(DTC_PR['recall'], DTC_PR['precision'],color='green',label='DecisionTree=%f'%DTC_Accuracy)
PR_ax.plot(LightGBM_PR['recall'], LightGBM_PR['precision'],color='blue',label='LightGBM=%f'%LightGBM_Accuracy)
PR_ax.plot(Logistic_PR['recall'], Logistic_PR['precision'],color='yellow',label='Logistic=%f'%Logistic_Accuracy)
PR_ax.plot(RondomForest_PR['recall'], RondomForest_PR['precision'],color='purple',label='RondomForest=%f'%RondomForest_Accuracy)
PR_ax.plot(SVM_PR['recall'], SVM_PR['precision'],color='cyan',label='SVM=%f'%SVM_Accuracy)
PR_ax.plot(XGBoost_PR['recall'], XGBoost_PR['precision'],color='pink',label='XGBoost=%f'%XGBoost_Accuracy)
PR_ax.plot([0,1],[1,0],linestyle='-.',color='black')
PR_ax.set_xlabel('Recall')
PR_ax.set_ylabel('Precision')
plt.legend(loc="best")
plt.show()
#绘制ROC曲线
import matplotlib.pyplot as plt
ROC_curve=plt.figure(dpi=300)
ROC_ax=ROC_curve.add_subplot(111)
ROC_ax.set_title('ROC Curve')
ROC_ax.plot(CatBoost_ROC['fpr'], CatBoost_ROC['tpr'],color='red',label='CatBoost=%f'%CatBoost_AUC)
ROC_ax.plot(DTC_ROC['fpr'], DTC_ROC['tpr'],color='green',label='DecisionTree=%f'%DTC_AUC)
ROC_ax.plot(LightGBM_ROC['fpr'], LightGBM_ROC['tpr'],color='blue',label='LightGBM=%f'%LightGBM_AUC)
ROC_ax.plot(Logistic_ROC['fpr'], Logistic_ROC['tpr'],color='yellow',label='Logistic=%f'%Logistic_AUC)
ROC_ax.plot(RondomForest_ROC['fpr'], RondomForest_ROC['tpr'],color='purple',label='RondomForest=%f'%RondomForest_AUC)
ROC_ax.plot(SVM_ROC['fpr'], SVM_ROC['tpr'],color='cyan',label='SVM=%f'%SVM_AUC)
ROC_ax.plot(XGBoost_ROC['fpr'], XGBoost_ROC['tpr'],color='pink',label='XGBoost=%f'%XGBoost_AUC)
ROC_ax.plot([0,1],[0,1],linestyle='-.',color='black')
ROC_ax.set_xlim([-0.05, 1.0])
ROC_ax.set_ylim([0, 1.05])
ROC_ax.set_xlabel('FPR')
ROC_ax.set_ylabel('TPR')
plt.legend(loc="best")
plt.show()
#保存模型-输入[batchs,70],输出[batch,1]
MLModel=[Logistic_ML,SVM_ML,DTC_ML,Forest_ML,XGBoost_ML,LightGBM_ML,CatBoost_ML]
MLModelStr=['Logistic.pkl','SVM.pkl','DecisionTree.pkl','Forest.pkl','XGBoost.pkl','LightGBM.pkl','CatBoost.pkl']
try:
for model,modelstr in zip(MLModel,MLModelStr):
joblib.dump(model,'ML Models/Trainer/'+modelstr)
print('模型保存成功!')
except:
print('模型保存异常!!!')
模型保存成功!
载入SHAP库计算特征重要性
#使用PCA算法计算95%可解释方差比维度数
from sklearn.decomposition import PCA
PCA=PCA(n_components=0.95)
Methylation_99=PCA.fit_transform(MLRunData) #数据降维
Methylation_99.shape
(6413, 33)
使用SHAP库计算获得CatBoost模型特征重要性
#复现CatBoost模型训练额测试数据集
CatBoost_train,CatBoost_test,CatBoost_trainlabel,CatBoost_testlabel=train_test_split(MLRunData,ReLabel,stratify=ReLabel,train_size=0.7,random_state=2024)
import shap
CatExplainer=shap.TreeExplainer(CatBoost_ML)
CatBoost_shapvalues=CatExplainer.shap_values(CatBoost_test)
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33)
shap.summary_plot(CatBoost_shapvalues,CatBoost_test,max_display=33,plot_type='bar')
#绘制heatmap图
SHAP_CatBoost=CatExplainer(CatBoost_test)
plt.figure(dpi=300)
shap.plots.heatmap(SHAP_CatBoost,max_display=34)
plt.show()
#绘制Decision图
expected_value=CatExplainer.expected_value
shap.decision_plot(expected_value,CatBoost_shapvalues,CatBoost_test.columns,feature_display_range=slice(None, -34, -1))
shap.plots.scatter(SHAP_CatBoost[:,'age'])
shap.plots.scatter(SHAP_CatBoost[:,'age'],color=SHAP_CatBoost)
shap.plots.scatter(SHAP_CatBoost[:,'gender_encode'],color=SHAP_CatBoost)
#输出SHAP值
SHAPData=DataFrame(CatBoost_shapvalues)
SHAPData.to_excel('Data/SHAP Values/CatBoost_SHAP_Data.xlsx','UFT-8')
SHAPData.to_csv('Data/SHAP Values/CatBoost_SHAP_Data.csv')
使用LIME(Local Interpretable Model-agnostic Explanations)对SVM模型进行模型解释性。
#转化为Numpy数组
import numpy as np
TestDataLIME=np.array(CatBoost_test)
TestDataLIME
array([[0.46083596, 0.72218471, 0.67574463, ..., 0.36230225, 0.66666667,
0. ],
[0.38295897, 0.50947919, 0.61430678, ..., 0.46848894, 0.27192982,
1. ],
[0.33958682, 0.69191957, 0.65616158, ..., 0.40703271, 0.72807018,
1. ],
...,
[0.33958682, 0.59925265, 0.61928701, ..., 0.34645684, 0.47368421,
1. ],
[0.42417219, 0.72249519, 0.67450701, ..., 0.44286982, 0.18421053,
0. ],
[0.39630923, 0.69161445, 0.66946837, ..., 0.41543005, 0.27116053,
0. ]])
#构建LIME解释器
from lime.lime_tabular import LimeTabularExplainer
CatBoost_LIMEExplainer=LimeTabularExplainer(training_data=TestDataLIME,mode='classification',feature_names=CatBoost_test.columns,class_names=[0,1], discretize_continuous=True)
CatBoost_test.shape
(1924, 72)
#提取卒中患者和健康人数据——为数据生成解释
LIMEData=CatBoost_test
LIMEData['disease_encode']=np.array(CatBoost_testlabel)
#LIMEData=pd.concat([CatBoost_test,CatBoost_testlabel],axis=1,ignore_index=False) #合并数据
Contral_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==0,:] #正常组数据
Contral_DataSample=Contral_Data.iloc[0,:-1]
Stroke_Data=LIMEData.loc[LIMEData.loc[:,'disease_encode']==1,:] #正常组数据
Stroke_DataSample=Stroke_Data.iloc[1,:-1]
#为健康人生成可解释性
Contral_Explain=CatBoost_LIMEExplainer.explain_instance(Contral_DataSample,CatBoost.predict_proba,num_features=33)
#可视化解释结果
Contral_Explain.show_in_notebook(show_table=True, show_all=False)
Contral_Explain.as_pyplot_figure()
Control_List=Contral_Explain.as_list()
Control_STR=[]
for feature, weight in Control_List:
print(f"特征名称: {feature}, 权重: {weight}")
Control_STR.append(feature)
特征名称: age > 0.63, 权重: 0.14865150574161518 特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425 特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313 特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709 特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463 特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577 特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008 特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517 特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457 特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256 特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686 特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877 特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827 特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702 特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703 特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248 特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338 特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897 特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364 特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131 特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636 特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192 特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126 特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949 特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211 特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464 特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171 特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255 特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191 特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926 特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678 特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464 特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
#为卒中患者生成可解释性
Stroke_Explain=CatBoost_LIMEExplainer.explain_instance(Stroke_DataSample,CatBoost.predict_proba,num_features=29)
#可视化解释结果
Stroke_Explain.show_in_notebook(show_table=True, show_all=False)
Stroke_Explain.as_pyplot_figure()
Stroke_List=Stroke_Explain.as_list()
Stroke_STR=[]
for feature, weight in Control_List:
print(f"特征名称: {feature}, 权重: {weight}")
Stroke_STR.append(feature)
特征名称: age > 0.63, 权重: 0.14865150574161518 特征名称: 0.50 < cg05876899 <= 0.52, 权重: 0.04953711744601425 特征名称: cg21459545 > 0.75, 权重: -0.0452230781670313 特征名称: cg08291693 <= 0.49, 权重: 0.03840864053911709 特征名称: 0.79 < cg22328457 <= 0.94, 权重: -0.024663944916912463 特征名称: cg01446576 <= 0.53, 权重: 0.023327759322679577 特征名称: 0.34 < cg00328972 <= 0.36, 权重: -0.01996306540110008 特征名称: cg01717973 <= 0.41, 权重: -0.01708062697242517 特征名称: 0.70 < cg00958409 <= 0.73, 权重: -0.016727337124053457 特征名称: 0.55 < cg01550055 <= 0.60, 权重: -0.013410851190519256 特征名称: cg09134165 > 0.62, 权重: -0.012931723407679686 特征名称: cg19214916 > 0.91, 权重: -0.011827585792960877 特征名称: 0.53 < cg20493887 <= 0.57, 权重: -0.01106614834013827 特征名称: 0.65 < cg21939789 <= 0.71, 权重: 0.010721621273459702 特征名称: 0.44 < cg00716675 <= 0.46, 权重: -0.01034117860684703 特征名称: cg01101221 <= 0.59, 权重: 0.010208519532552248 特征名称: 0.62 < cg03977822 <= 0.66, 权重: 0.009724454790564338 特征名称: cg12914043 > 0.56, 权重: 0.00939769877317897 特征名称: gender_encode <= 0.00, 权重: 0.009243658815866364 特征名称: cg09834142 > 0.67, 权重: 0.008717453580699131 特征名称: 0.85 < cg09229960 <= 0.89, 权重: 0.008543352070252636 特征名称: cg15534364 > 0.42, 权重: 0.008160416432172192 特征名称: 0.66 < cg00817100 <= 0.68, 权重: -0.00795961311381126 特征名称: 0.55 < cg17488844 <= 0.64, 权重: -0.007565930010584949 特征名称: 0.31 < cg01522249 <= 0.39, 权重: -0.00742375707147211 特征名称: cg27260927 > 0.72, 权重: -0.007116079561404464 特征名称: 0.55 < cg17917970 <= 0.71, 权重: -0.006875061533805171 特征名称: cg27353825 > 0.44, 权重: -0.006495167447585255 特征名称: cg19267533 > 0.59, 权重: 0.005410242932241191 特征名称: cg00962755 > 0.44, 权重: -0.005133920381963926 特征名称: cg25967516 > 0.56, 权重: 0.005076072700733678 特征名称: 0.61 < cg09060772 <= 0.64, 权重: 0.004888797838171464 特征名称: 0.41 < cg27636290 <= 0.45, 权重: -0.004866121466454682
Stroke_STR
['age > 0.63', '0.50 < cg05876899 <= 0.52', 'cg21459545 > 0.75', 'cg08291693 <= 0.49', '0.79 < cg22328457 <= 0.94', 'cg01446576 <= 0.53', '0.34 < cg00328972 <= 0.36', 'cg01717973 <= 0.41', '0.70 < cg00958409 <= 0.73', '0.55 < cg01550055 <= 0.60', 'cg09134165 > 0.62', 'cg19214916 > 0.91', '0.53 < cg20493887 <= 0.57', '0.65 < cg21939789 <= 0.71', '0.44 < cg00716675 <= 0.46', 'cg01101221 <= 0.59', '0.62 < cg03977822 <= 0.66', 'cg12914043 > 0.56', 'gender_encode <= 0.00', 'cg09834142 > 0.67', '0.85 < cg09229960 <= 0.89', 'cg15534364 > 0.42', '0.66 < cg00817100 <= 0.68', '0.55 < cg17488844 <= 0.64', '0.31 < cg01522249 <= 0.39', 'cg27260927 > 0.72', '0.55 < cg17917970 <= 0.71', 'cg27353825 > 0.44', 'cg19267533 > 0.59', 'cg00962755 > 0.44', 'cg25967516 > 0.56', '0.61 < cg09060772 <= 0.64', '0.41 < cg27636290 <= 0.45']
获取LIME核SHAP数据交集
SHAP_CG=["cg05876899","cg00958409","cg08882363","cg21459545","cg00328972","cg01550055","cg21939789","cg01446576","cg17917970","cg20493887",
"cg22328457","cg12914043","cg08291693","cg09834142","cg00941229","cg01717973","cg06834235","cg09134165","cg00817100","cg00716675",
"cg17488844","cg07810091","cg12377701","cg23429746","cg32524254","cg01370179","cg07086565","cg13192819","cg19214916","cg24606370",
"cg25967516","cg09307104","cg05876899"]
LIME_CG=['cg05876899', 'cg21459545', 'cg08291693', 'cg22328457', 'cg01446576','cg00328972', 'cg01717973', 'cg00958409', 'cg01550055', 'cg09134165',
'cg19214916', 'cg20493887', 'cg21939789', 'cg00716675', 'cg01101221','cg03977822', 'cg12914043', 'cg09834142', 'cg09229960', 'cg15534364',
'cg00817100', 'cg17488844', 'cg01522249', 'cg27260927', 'cg17917970','cg27353825', 'cg19267533', 'cg00962755', 'cg25967516', 'cg09060772',
'cg27636290','cg23429746','cg05876899']
Feature_CG=set(SHAP_CG)&set(LIME_CG) #计算交集
Feature_CG
{'cg00328972',
'cg00716675',
'cg00817100',
'cg00958409',
'cg01446576',
'cg01550055',
'cg01717973',
'cg05876899',
'cg08291693',
'cg09134165',
'cg09834142',
'cg12914043',
'cg17488844',
'cg17917970',
'cg19214916',
'cg20493887',
'cg21459545',
'cg21939789',
'cg22328457',
'cg23429746',
'cg25967516'}
from matplotlib import pyplot as plt
from matplotlib_venn import venn2
plt.figure(figsize=(8, 8))
venn2([set(SHAP_CG),set(LIME_CG)], set_labels=('Features in SHAP', 'Features in LIME'))
plt.show()
对数据进行统计学分析
StatData=MethylationData.loc[:,list(Feature_CG)+['gender','age','disease']]
StatData.head(10)
| cg00716675 | cg00958409 | cg01550055 | cg17488844 | cg08291693 | cg25967516 | cg21459545 | cg09834142 | cg19214916 | cg05876899 | ... | cg00817100 | cg01717973 | cg21939789 | cg12914043 | cg09134165 | cg22328457 | cg01446576 | gender | age | disease | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 0 | -3.837361 | -2.616463 | -1.585160 | -1.909169 | 2.324893 | -0.052001 | 0.384596 | -0.601375 | 0.200630 | 2.921730 | ... | -2.010774 | -3.052934 | -0.164336 | -0.866223 | -1.288795 | -1.564056 | -2.570129 | F | 78.0 | stroke |
| 1 | -4.247583 | -2.767818 | -1.247949 | -3.938986 | 3.100385 | -1.657722 | -0.645381 | -3.938986 | -1.557086 | 3.228904 | ... | -1.543240 | -3.886935 | -3.314031 | -3.790034 | -3.256540 | 0.000000 | -3.744756 | M | 52.0 | stroke |
| 2 | -3.993781 | -2.375206 | -1.536363 | -4.489850 | 2.749976 | -1.734052 | -0.519260 | -4.178048 | -1.219330 | 3.076403 | ... | -1.710727 | -5.273603 | -2.698069 | -3.201956 | -3.744756 | -3.580953 | -4.051632 | M | 92.0 | stroke |
| 3 | -3.790034 | -2.555085 | -1.757764 | -1.695385 | 3.076403 | -1.125184 | 0.281794 | -0.795272 | -0.376304 | 3.374769 | ... | -2.288480 | -4.178048 | -0.540693 | -2.120936 | 0.048000 | -2.441207 | -4.585271 | F | 87.0 | stroke |
| 4 | -4.402578 | -2.388119 | -1.726235 | -3.580953 | 3.007447 | -2.079779 | -0.397057 | -3.580953 | -1.848299 | 3.228904 | ... | -2.337277 | -3.993781 | -2.785946 | -3.256540 | -3.149987 | -3.744756 | -4.247583 | M | 87.0 | stroke |
| 5 | -3.790034 | -2.230092 | -1.253727 | -4.322159 | 2.881228 | -1.856830 | 0.003999 | -2.985388 | -1.185564 | 3.314031 | ... | -2.020410 | -4.112908 | -2.648415 | -2.804368 | -3.100385 | -3.701353 | -4.112908 | M | 41.0 | stroke |
| 6 | -3.790034 | -2.715111 | -1.271175 | -0.772045 | 2.767818 | -0.954183 | 0.519260 | 0.019997 | -0.924494 | 3.124904 | ... | -1.900305 | -3.837361 | -0.401218 | -1.093021 | 0.156286 | -1.114405 | -4.585271 | F | 78.0 | stroke |
| 7 | -2.749976 | -2.749976 | -1.324576 | -3.790034 | 3.701353 | -2.441207 | -0.654254 | -3.580953 | -2.496772 | 3.284902 | ... | -1.330612 | -4.051632 | -2.482634 | -3.837361 | -3.076403 | -3.314031 | -4.322159 | M | 80.0 | stroke |
| 8 | -2.427712 | -2.648415 | -1.157883 | -4.690541 | 3.314031 | -2.804368 | -0.426255 | -4.247583 | -1.613763 | 3.201956 | ... | -1.146922 | -4.322159 | -2.401172 | -3.406479 | -3.406479 | 0.000000 | -4.247583 | M | 69.0 | stroke |
| 9 | -2.842141 | -2.540233 | -0.974195 | -5.773449 | 3.256540 | -2.511081 | 0.084033 | -4.112908 | -1.373471 | 3.543689 | ... | -1.348848 | -3.886935 | -2.570129 | -3.100385 | 0.000000 | 0.000000 | -3.659672 | M | 75.0 | stroke |
10 rows × 24 columns
#进行正态性检验-基于D’Agostino and Pearson’s test
from scipy.stats import normaltest
Normal_Name,Normal_stat,Normal_Pvalue=[],[],[]
for normal_name in list(Feature_CG)+['age']:
stats,p_value=normaltest(StatData.loc[:,normal_name]) #正态检验
Normal_Name.append(normal_name)
Normal_stat.append(stats)
Normal_Pvalue.append(p_value)
NormalResut=DataFrame()
NormalResut['Features']=Normal_Name
NormalResut['Stats']=Normal_stat
NormalResut['P value']=Normal_Pvalue
NormalResut
| Features | Stats | P value | |
|---|---|---|---|
| 0 | cg23429746 | 128338.014578 | 0.000000e+00 |
| 1 | cg19214916 | 2722.890099 | 0.000000e+00 |
| 2 | cg01717973 | 2679.681287 | 0.000000e+00 |
| 3 | cg21459545 | 3691.925554 | 0.000000e+00 |
| 4 | cg20493887 | 621.263340 | 1.242739e-135 |
| 5 | cg01550055 | 881.708181 | 3.463389e-192 |
| 6 | cg01446576 | 3764.295928 | 0.000000e+00 |
| 7 | cg05876899 | 1797.616451 | 0.000000e+00 |
| 8 | cg00817100 | 5265.657589 | 0.000000e+00 |
| 9 | cg09134165 | 1570.401594 | 0.000000e+00 |
| 10 | cg09834142 | 4664.772845 | 0.000000e+00 |
| 11 | cg08291693 | 3407.019911 | 0.000000e+00 |
| 12 | cg17917970 | 943.887704 | 1.089906e-205 |
| 13 | cg25967516 | 335.598808 | 1.335503e-73 |
| 14 | cg22328457 | 701.152844 | 5.579490e-153 |
| 15 | cg00958409 | 7688.564578 | 0.000000e+00 |
| 16 | cg12914043 | 380.433626 | 2.454074e-83 |
| 17 | cg00716675 | 5795.308748 | 0.000000e+00 |
| 18 | cg17488844 | 865.716145 | 1.028318e-188 |
| 19 | cg21939789 | 46963.872736 | 0.000000e+00 |
| 20 | cg00328972 | 3194.712049 | 0.000000e+00 |
| 21 | age | 1563.362527 | 0.000000e+00 |
NormalResut.to_excel('Data/Analysis Data/NormalTest.xlsx') #输出正态检验结果
#绘制分布图
import seaborn as sns
for name in list(Feature_CG)+['age']:
kde=plt.figure()
sns.distplot(StatData.loc[:,name])
plt.show()
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
`distplot` is a deprecated function and will be removed in seaborn v0.14.0. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `histplot` (an axes-level function for histograms). For a guide to updating your code to use the new functions, please see https://gist.github.com/mwaskom/de44147ed2974457ad6372750bbe5751
#使用Mann-Whitney U对卒中患者、对照组进行差异分析
from scipy.stats import mannwhitneyu,kruskal
Diff_name,Mann_W_stats,Mann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Control=StatData.loc[StatData.loc[:,'disease']=='control',:]
for name in list(Feature_CG)+['age']:
mann_stats,mann_pvalue=mannwhitneyu(Stroke.loc[:,name],Control.loc[:,name],alternative='two-sided')
Diff_name.append(name)
Mann_W_stats.append(mann_stats)
Mann_W_pvalue.append(mann_pvalue)
DiffResult=DataFrame()
DiffResult['Features']=Diff_name
DiffResult['Mann-Whitney U Stats']=Mann_W_stats
DiffResult['Mann-Whitney U Pvalue']=Mann_W_pvalue
DiffResult=DiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
DiffResult
| Features | Mann-Whitney U Stats | Mann-Whitney U Pvalue | |
|---|---|---|---|
| 7 | cg05876899 | 757768.0 | 6.339030e-41 |
| 21 | age | 746114.5 | 6.539961e-38 |
| 15 | cg00958409 | 212382.0 | 4.768737e-29 |
| 3 | cg21459545 | 223154.5 | 1.027115e-26 |
| 5 | cg01550055 | 683678.0 | 8.623696e-24 |
| 14 | cg22328457 | 267557.0 | 4.269751e-19 |
| 6 | cg01446576 | 283942.5 | 1.678612e-15 |
| 12 | cg17917970 | 300048.5 | 2.240491e-13 |
| 9 | cg09134165 | 309546.0 | 4.232856e-12 |
| 20 | cg00328972 | 315722.5 | 6.231854e-11 |
| 2 | cg01717973 | 591836.5 | 3.151485e-09 |
| 11 | cg08291693 | 341291.0 | 7.604157e-08 |
| 0 | cg23429746 | 367458.5 | 2.721422e-05 |
| 1 | cg19214916 | 377703.0 | 1.885910e-04 |
| 8 | cg00817100 | 536568.5 | 6.124156e-04 |
| 4 | cg20493887 | 395897.5 | 3.540958e-03 |
| 17 | cg00716675 | 415576.0 | 4.261281e-02 |
| 13 | cg25967516 | 497331.5 | 9.736992e-02 |
| 16 | cg12914043 | 496806.5 | 1.022572e-01 |
| 18 | cg17488844 | 488567.0 | 2.066917e-01 |
| 10 | cg09834142 | 486461.0 | 2.428824e-01 |
| 19 | cg21939789 | 476969.0 | 4.592971e-01 |
DiffResult.to_excel('Data/Analysis Data/StrokeVSControl.xlsx')
#数据标准化和缩放
CorrData=StatData.loc[:,['age','cg05876899']]
Corr_name=CorrData.columns
CorrStand=StandardScaler() #数据标准化
CorrStand=CorrStand.fit_transform(CorrData)
MinMaxCorr=MinMaxScaler(feature_range=(0,1)) #数据缩放
MinMax=MinMaxCorr.fit_transform(CorrStand)
CorrDataST=DataFrame(MinMax)
CorrDataST.columns=Corr_name
CorrDataST
| age | cg05876899 | |
|---|---|---|
| 0 | 0.684211 | 0.495571 |
| 1 | 0.456140 | 0.520210 |
| 2 | 0.807018 | 0.507977 |
| 3 | 0.763158 | 0.531910 |
| 4 | 0.763158 | 0.520210 |
| ... | ... | ... |
| 6408 | 0.000000 | 0.430502 |
| 6409 | 0.652632 | 0.511868 |
| 6410 | 0.605263 | 0.502446 |
| 6411 | 0.298246 | 0.534453 |
| 6412 | 0.131579 | 0.515939 |
6413 rows × 2 columns
#绘制age与cg05876899散点图
from scipy.stats import spearmanr
corr,p_value=spearmanr(CorrDataST.loc[:,'age'],CorrDataST.loc[:,'cg05876899'])
print('Spearman相关系数为:{0},P值为:{1}'.format(corr,p_value))
plt.figure(dpi=300)
sns.jointplot(data=CorrDataST,x='age',y='cg05876899')
plt.show()
Spearman相关系数为:-0.09646944046883249,P值为:9.790722731741588e-15
<Figure size 1800x1200 with 0 Axes>
plt.figure(dpi=300)
sns.barplot(StatData,x='disease',y='cg05876899',errorbar="ci")
plt.show()
#绘制箱型图
for name in list(DiffResult['Features']):
Box=plt.figure(dpi=300)
sns.boxplot(StatData,x='disease',y=name,showmeans=True)
plt.show()
#使用Mann-Whitney U对男性患者和女性患者进行差异分析
from scipy.stats import mannwhitneyu,kruskal
SexDiff_name,SexMann_W_stats,SexMann_W_pvalue=[],[],[]
Stroke=StatData.loc[StatData.loc[:,'disease']=='stroke',:]
Man=Stroke.loc[Stroke.loc[:,'gender']=='M',:]
Feman=Stroke.loc[Stroke.loc[:,'gender']=='F',:]
for name in list(Feature_CG)+['age']:
mann_stats,mann_pvalue=mannwhitneyu(Man.loc[:,name],Feman.loc[:,name],alternative='two-sided')
SexDiff_name.append(name)
SexMann_W_stats.append(mann_stats)
SexMann_W_pvalue.append(mann_pvalue)
SexDiffResult=DataFrame()
SexDiffResult['Features']=SexDiff_name
SexDiffResult['Mann-Whitney U Stats']=SexMann_W_stats
SexDiffResult['Mann-Whitney U Pvalue']=SexMann_W_pvalue
SexDiffResult=SexDiffResult.sort_values('Mann-Whitney U Pvalue',ascending=True)
SexDiffResult
| Features | Mann-Whitney U Stats | Mann-Whitney U Pvalue | |
|---|---|---|---|
| 0 | cg23429746 | 55.0 | 1.184262e-24 |
| 10 | cg09834142 | 57.0 | 1.280125e-24 |
| 18 | cg17488844 | 57.5 | 1.295483e-24 |
| 19 | cg21939789 | 71.0 | 2.245348e-24 |
| 3 | cg21459545 | 72.5 | 2.394466e-24 |
| 16 | cg12914043 | 91.0 | 4.948103e-24 |
| 13 | cg25967516 | 129.5 | 2.259475e-23 |
| 1 | cg19214916 | 156.5 | 6.443147e-23 |
| 9 | cg09134165 | 202.0 | 3.474110e-22 |
| 12 | cg17917970 | 310.0 | 1.973555e-20 |
| 14 | cg22328457 | 1397.5 | 4.413451e-07 |
| 21 | age | 1713.5 | 1.300201e-04 |
| 11 | cg08291693 | 3530.5 | 1.314165e-03 |
| 7 | cg05876899 | 3309.5 | 1.841224e-02 |
| 4 | cg20493887 | 2157.5 | 3.532568e-02 |
| 15 | cg00958409 | 3086.5 | 1.356811e-01 |
| 2 | cg01717973 | 2411.5 | 2.623936e-01 |
| 20 | cg00328972 | 2863.0 | 5.301055e-01 |
| 5 | cg01550055 | 2559.5 | 5.848453e-01 |
| 8 | cg00817100 | 2790.0 | 7.316775e-01 |
| 17 | cg00716675 | 2656.0 | 8.630851e-01 |
| 6 | cg01446576 | 2742.5 | 8.735049e-01 |
SexDiffResult.to_excel('Data/Analysis Data/SexDiffResult.xlsx')
plt.figure(dpi=300)
sns.barplot(Stroke,x='gender',y='cg23429746',errorbar="ci")
plt.show()
#绘制箱型图
for name in SexDiffResult['Features']:
Box=plt.figure(dpi=300)
sns.boxplot(Stroke,x='gender',y=name,showmeans=True)
plt.show()
import matplotlib.pyplot as plt
import seaborn as sns
for cg_name in list(Feature_CG)+['age']:
box=plt.figure(dpi=300)
sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
plt.show()
#提取卒中患者VS对照组与男性患者与女性患者甲基化数据取交集_[Top 10]
CG_StrokeVSControl=['cg05876899', 'cg00958409', 'cg21459545', 'cg01550055', 'cg22328457', 'cg01446576', 'cg17917970',
'cg09134165', 'cg00328972','cg01717973']
CG_StrokeManVSFeman=['cg23429746', 'cg09834142', 'cg17488844', 'cg21939789', 'cg21459545', 'cg12914043', 'cg25967516',
'cg19214916', 'cg09134165', 'cg17917970']
Get_DiffCG=set(CG_StrokeVSControl)&set(CG_StrokeManVSFeman)
print(Get_DiffCG)
{'cg09134165', 'cg21459545', 'cg17917970'}
#绘制韦恩图
plt.figure(figsize=(6,8))
venn2([set(CG_StrokeVSControl),set(CG_StrokeManVSFeman)],set_labels=('Stroke group vs control group','Male stroke group vs female stroke group'))
plt.show()
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
box=plt.figure(dpi=300)
sns.boxplot(StatData,x='disease',y=cg_name,hue='gender',showmeans=True)
plt.show()
#绘制条形图
for cg_name in list(Get_DiffCG)+['cg23429746','cg05876899']:
box=plt.figure(dpi=300)
sns.barplot(StatData,x='disease',y=cg_name,hue='gender',errorbar="ci") #95%CI
plt.show()